data science capstone project ideas 2021

UCSD Data Science Capstone Projects: 2021-2022

Introduction.

• The title and abstract,
• A link to the project's website
• A link to the project's code repository

Areas of Study

Project Details

Fair policing, traffic policing and its relationship with income.

• Group members: Ronaldo Romano, Jason Sheu, Leon Kuo
• Project GitHub -->
• Project GitHub
• Project Report

Abstract: Policing is a rather mixed affair hinging on a number of different factors with some encounters being relatively short and simple while others are more tense and hostile. There are many different reasons for why police make the decisions that they make, either rightfully or wrongfully, many of which aren't directly observable or easily determined within police data. Some of these factors may include suspicion of drivers doing illegal activity, misdemeanors, or having unconscious bias against individuals that appear to fit their mental description of what a criminal may look like. While racial prejudice is one of the more striking factors to point at when considering how or why encounters between individuals and police officers go differently, decisions seemly motivated by racial bias may potentially be confounded with aspects of perceived social class of the individual. We investigate this confounding.

Text Mining and NLP

Model analysis of stock price trend predictions based on financial news.

• Group members: Liuyang Zheng, Yunhan Zhang, Mingjia Zhu

Abstract: Financial news is an important source for people to learn information in financial field, such as the variation in stock market. News could also be a key factor to forecast change in the stock market. In this report, we introduce different methods we tried to predict the change of stock price by using financial news, including Bag-of-Words, AutoPhrase, LSTM, and BERT. Our experiments demonstrate that BERT outperforms other models.

Utilizing AutoPhrase on Computer Science papers over time

• Group members: Jason Lin, Cameron Brody, James Yu

Abstract: Phrase mining is a useful tool to extract quality phrases from large text corpora. Previous work on this topic, such as AutoPhrase, demonstrates its effectiveness against baseline methods by using precision-recall as a metric. Our goal is to extend this work by analyzing how AutoPhrase phrases change over time, as well as how phrases are connected with each other by using network visualizations. This will be done through exploratory data analysis, along with a classification model utilizing individual phrases to predict a specific year range.

Codenames AI

• Group members: Xuewei Yan, Cameron Shaw, Yongqing Li

Abstract: Codenames is a popular board game that relies on word association and its ultimate goal is to connect multiple words together with a single clue word. In this paper, we construct a system that incorporates artificial intelligence into the game to allow communication between humans and AI as well as providing the capability of replacing human effort in creating such a clue word. Our project utilized three types of word relationship measurements from Word2Vec, GloVe, and WordNet, to design and understand word relationships used in this game. An AI system is built on each measurement and tested on both AI-AI and AI-Human communication performance. We evaluate the performance with each system’s average speed in finishing a game as well as its ability to accurately identify their team words. The AI-AI team performance demonstrates outstanding efficiency for AI to manage this game, and the best performing measurement is able to achieve a 60% accuracy in its communication between AI and Human.

Spam Detection Using Natural Language Processing

• Group members: Jonathan Tanoto

Abstract: Building a spam detection algorithm by utilizing Natural Language Processing to extract features associated with spam emails. Deep Learning methods as well as word-to-vector transformation are used to create a spam email classifier.

Blockchain / Smart-Contracts

An exploration on medical records using blockchain technology.

• Group members: Ruiwei Wan, Yifei Wang

Abstract: In this project, we set out to explore the application of blockchain technology to Electronical Health Records systems. As we are prototyping the blockchain applications on the Electronic Medical Records System using our proposed Medcoin application, we encountered several challenges. After careful evaluations and discussions, we decide to turn our project into an exploration of the pros and cons of using blockchain applications in the Electronic Health Records system. We find that the proposed authorization contract could not meet the required authentication and testification functions of EHR, which are the two essential components for EHR, we, therefore, stop in our prototyping and in our report provide a discussion of advantages and disadvantages of using Blockchain for EHR systems. And due to the privacy issue of medical records, we also find the authorization smart contract proposal infeasible and exhibits lack of considerations. Our prototyping of smart contract failure could serve as a valuable lesson to why centralized application could be more proper to Medical Records related system design.

spatiotemporal machine learning

Uncertainty quantification and deep learning for scalable spatiotemporal analysis.

• Group members: Kailing Ding, Judy Jin, Derek Leung, Miles Labrador

Abstract: In spatiotemporal forecasting, deep learning models need to not only make predictions but also quantify their predictions' certainty (uncertainty). For example, consider a stock automatic trading system where a machine learning model predicts the stock price. A point prediction from the model might be dramatically different from the real value because of the high stochasticity of the stock market. But, on the other hand, if the model could estimate the range which guarantees to cover the true value with high probability, the trading system could compute the best and worst rewards and make more sensible decisions. And this is where conformal prediction technique comes in, which is a technique for quantifying such uncertainties for models. In the paper, we seek to evaluate the performance and quality of conformal quantile regression that embeds uncertainty metrics into their output. Beyond this, we will also seek to contribute to the torchTS library by implementing a data loader class. This class will be designed to preprocess and split up data into training, calibration, and test sets in a more consistent format for our models to be more easily applied. Lastly, we aim to improve the torchTS library API documentation to present the library's functionality in an easily understood way as well as present users with examples of torchTS' spatiotemporal analysis methods being used.

High-dimensional Statistical Learning, Causal Inference, Robust ML, Fair ML

Post-prediction inference on political twitter.

• Group members: Luis Ledezma-Ramos, Dylan Haar, Alicia Gunawan

Abstract: Having observed data seems to be a necessary requirement to conduct inference, but what happens when observed outcomes cannot easily be obtained? The simplest practice seems to proceed with using predicted outcomes, but without any corrections this can result in issues like bias and incorrect standard errors. Our project studies a correction method for inference conducted on predicted, not observed outcomes—called post-prediction inference—through the lens of political data. We are investigating the kinds of phrases or words in a tweet that will most strongly indicate a person’s political alignment to US politics. We have discovered that these correction techniques are promising in their ability to correct for post-prediction inference in the field of political science.

NFL-Analysis

• Group members: Jonathan Langley, Sujeet Yeramareddy, Yong Liu

Abstract: After researching about a new inference correction approach called post-prediction inference, we chose to apply it to sports analysis based on NFL games. We designed a model that can predict the Spread of a football game, such as which team will win and what the margin of their victory will be. We then analyzed the most/least important features so that we can accurately correct inference for these variables in order to more accurately understand their impact on our response variable, Spread.

Machine Learning (TBA)

Investigation on latent dirichlet allocation.

• Group members: Duha Aldebakel, Rui Zhang, Anthony Limon, Yu Cao

Abstract: We explore both Markov Chain Monte Carlo algorithms and variational inference methods for Latent Dirichlet Allocation (LDA), a generative probabilistic topic model for data such as text data. LDA is a generative probabilistic topic model, meaning we treat data as observations that arise from a generative probabilistic process including hidden variables, i.e. structure we want to find in the data. Topic modelling allows us to fulfill algorithmic needs to organize, understand, and annotate documents according to the discovered structure. For text data, hidden variables reflect the thematic structure of a corpus that we don't have access to, we only have access to our observations which are the documents of the collection themselves. Our aim is to infer this hidden structure through posterior inference, that is, we want to compute the conditional distribution of the hidden variables given our observations, and we use our knowledge from Q1 about inference methods to solve this problem.

Wildfire and Environmental Data Analysis

Machine learning for physical systems, locating sound with machine learning.

• Group members: Raymond Zhao, Brady Zhou

Abstract: In this domain, we learned about the methods around localizing sound waves using special devices called microphone arrays. Broadly speaking, this device can figure what a sound is and where it came from. With the growing ubiquity of microphone devices, we find this to be a potentially useful use-case. The base case scenario method involves what is called "affine mapping" which is essentially another form of linear transformation. In this project, we decided to examine how machine learning techniques such as Neural Networks, Support Vector Machines, and Random Forest may benefit (or not benefit) in this field.

Environmental Monitoring, remote sensing, cyber-physical systems, Engineers for Exploration

E4e microfaune project.

• Group members: Jinsong Yang, Qiaochen Sun

Abstract: Nowadays, human activities such as wildfires and hunting have become the largest factor that would have serious negative effects on biodiversity. In order to deeply understand how anthropogenic activities deeply affect wildlife populations, field biologists utilize automated image classification driven by neural networks to get relevant biodiversity information from the images. However, for some small animals such as insects or birds, the camera could not work very well because of the small size of these animals. It is extremely hard for cameras to capture the movement and activities of small animals. To effectively solve this problem, passive acoustic monitoring (PAM) has become one of the most popular methods. We could utilize sounds we collect from PAM to train certain machine learning models which could tell us the fluctuation of biodiversity of all these small animals. The goal of the whole program is to test the biodiversity of these small animals (most of them are birds). However, the whole program could be divided into plenty of small parts. I and Jinsong will pay attention to the intermediate step of the program. The goal of our project is to generate subsets of audio recordings that have higher probability of vocalization of interest, which could help our labeling volunteer to save time and energy. The solutions could help us reduce down the amount of time and resources required to achieve enough training data for species-level classifiers. We perform the same thing with AID_NeurIPS_2021. Only the data is different between these two github. For this github, we use the peru data instead of Coastal_Reserve data.

• Group members: Harsha Jagarlamudi, Kelly Kong

Eco-Acoustic Event Detection: Classifying temporal presence of birds in recorded bird vocalization audio

• Group members: Alan Arce, Edmundo Zamora

Abstract: Leveraging "Deep Learning" methods to classify temporal presence birds in recorded bird vocalization audio. Using a hybrid CNN-RNN model, trained on audio data, in the interest of benefitting wildlife monitoring and preservation.

Pyrenote - User Profile Design & Accessible Data

• Group members: Dylan Nelson

Abstract: Pyrenote is a project in development by a growing group of student researchers here at UCSD. It's primary purpose is to allow anyone to contribute to research by labeling data in an intuitive and accessible way. Right now it is currently being used to develop a sort of voice recognition for birds. The goal is to make an algorithm that can strongly label data (say where in the clip a bird is calling and what bird is making the call). To do this, a very vast dataset is needed to be labeled. I worked mostly on the user experience side. Allowing them to interact with their labeling in new ways, such as keeping tabs on their progress and reaching goals. Developing a User Profile page was the primary source for receiving this data and was developed iteratively as a whole new page for the site

Pyrenote Webdeveloper

• Group members: Wesley Zhen

Abstract: The website, Pyrenote, is helping scientists track bird populations by identifying them using machine learning classifiers on publicly annotated audio recordings. I have implemented three features over the course of two academic quarters aimed at streamlining user experience and improving scalability. The added scalability will be useful for future projects as we start becoming more ambitious with the number of users we bring to the site.

Spread of Misinformation Online

Who is spreading misinformation and worries in twitter.

• Group members: Lehan Li, Ruojia Tao

Abstract: Spread of misinformation over social media posts challenges to daily information intake and exchange. Especially under current covid 19 pandemic, the disperse of misinformation regarding to covid 19 diseases and vaccination posts threats to individuals' wellbeing's and general publish health. The people's worries also increase with misinformation such as the shortage of food and water. This spread of misinformation also provide This project seeks to investigate the spread of misinformation over social media (Twitter) under covid 19 pandemic. wo main directions are investigated in the project. The first direction is the analysis of the effect of bot users on the spread of misinformation: We want to explore what is the role that robot user plays in spreading the misinformation. Where are the bot users located in the social network. The second direction is the sentiment analysis that examines users' attitudes towards misinformation: We want to see the spread of sentiment with different places in social networks. We also mixed the two directions: What is the relationship between bot-users with positive and negative emptions? Since online social medias users form social networks, the project also seeks to investigate the effect of social network on the above two topics. Moreover, the project is also interested in exploring the change in proportion of bot users and users' attitude towards misinformation as the social network becomes more concentrated and tightly connected.

Misinformation on Reddit

• Group members: Samuel Huang, David Aminifard

Abstract: As social media has grown in popularity, namely Reddit, its use for rapidly sharing information based on categories or topics (subreddits) has had massive implications for how people are usually exposed to information and the quality of the information they interact with. While Reddit has its benefits, e.g. providing instant access to - nearly - real time, categorized information, it has possibly played a role in worsening divisions and the spread of misinformation. Our results showed that subreddits with the highest proportions of misinformation posts tend to lean more towards politics and news. In addition, we found that despite the frequency of misinformation per subreddit, the average upvote ratio per submission seemed consistently high, which indicated that subreddits tend to be ideologically homogeneous.

The Spread of YouTube Misinformation Through Twitter

• Group members: Alisha Sehgal, Anamika Gupta

Abstract: In our Capstone Project, we explore the spread of misinformation online. More specifically, we look at the spread of misinformation across Twitter and YouTube because of the large role these two social media platforms play in the dissemination of news and information. Our main objectives are to understand how YouTube videos contribute to spreading misinformation on Twitter, evaluate how effectively YouTube is removing misinformation and if these policies also prevent users from engaging with misinformation. We take a novel approach of analyzing tweets, YouTube video captions, and other metadata using NLP to determine the presence of misinformation and investigate how individuals interact or spread misinformation. Our research focuses on the domain of public health as this is the subject of many conspiracies, varying opinions, and fake news.

Particle Physics

Understanding higgs boson particle jets with graph neural networks.

• Group members: Charul Sharma, Rui Lu, Bryan Ambriz

Abstract: Extending the content of last quarter of deep sets neural network, fully connected neural network classifier, adversarial deep set model and designed decorrelated tagger (DDT), we went a little bit further this quarter about picking up different layers in neural network like GENConv and EdgeConv. GENConv and EdgeConv play incredibly important roles here for boosting the performances of our basic GNN model. We also evaluated the performance of our model using ROC (Receiver-Operating Curve) curves describing AUC (Area Under the Curve). Meanwhile, based on previous experiences of project one and past project of particle physics domain, we decided to add one more section, exploratory data analysis in our project for conducting some basic theory, bootstrapping or common sense of our dataset. But we have not produced all the optimal outcomes so far even though we finished the EdgeConv part and for the following weeks, we would like to finish the GENConv and may try some other layers to find out the potential to increase the performance of our model.

Predicting a Particle's True Mass

• Group members: Jayden Lee, Dan Ngo, Isac Lee

Abstract: The Large Hadron Collider (LHC) collides protons traveling near light speed to generate high-energy collisions. These collisions produce new particles and have led to the discovery of new elementary particles (e.g., Higgs Boson). One key information to collect from this collision event is the structure of the particle jet, which refers to a group of collective spray of decaying particles that travel in the same direction, as accurately identifying the type of these jets - QCD or signal - play a crucial role in discovery of high-energy elementary particles like Higgs particle. There are several properties that determine jet type with jet mass being one of the strongest indicators in jet type classification. A previous study jet mass estimation, called “soft drop declustering,” has been one of the most effective methods in making rough estimations on the jet mass. With this in mind, we aim to implement machine learning in jet mass estimation through various neural network architectures. With data collected and processed by CERN, we implemented a model capable of improving jet mass prediction through jet features.

Mathematical Signal Processing (compression of deep nets, or optimization for data-science/ML)

Graph neural networks, graph neural network based recommender systems for spotify playlists.

• Group members: Benjamin Becze, Jiayun Wang, Shone Patil

Abstract: With the rise of music streaming services on the internet in the 2010’s, many have moved away from radio stations to streaming services like Spotify and Apple Music. This shift offers more specificity and personalization to users’ listening experiences, especially with the ability to create playlists of whatever songs that they wish. Oftentimes user playlists have a similar genre or theme between each song, and some streaming services like Spotify offer recommendations to expand a user’s existing playlist based on the songs in it. Using Node2vec and GraphSAGE graph neural network methods, we set out to create a recommender system for songs to add to an existing playlist by drawing information from a vast graph of songs we built from playlist co-occurrences. The result is a personalized song recommender based not only on Spotify’s community of playlist creators, but also the specific features within a song.

Dynamic Stock Industry Classification

• Group members: Sheng Yang

Abstract: Use Graph-based Analysis to Re-classify Stocks in China A-share and Improve Markowitz Portfolio Optimization

NLP, Misinformation

Hdsi faculty exploration tool.

• Group members: Martha Yanez, Sijie Liu, Siddhi Patel, Brian Qian

Abstract: The Halıcıoğlu Data Science Institute (HDSI) at University of California, San Diego is dedicated to the discovery of new methods and training of students and faculty to use data science to solve problems in the current world. The HDSI has several industry partners that are often searching for assistance to tackle their daily activities and need experts in different domain areas. Currently, there are around 55 professors affiliated to HDSI. They all have diverse research interests and have written numerous papers in their own fields. Our goal was to create a tool that allows HDSI to select the best fit from their faculty, based on their published work, to aid their industry partners in their specific endeavors. We did this with Natural Language Processing (NLP) by managing all the abstracts from the faculty’s published work and organizing them by topics. We will then obtained the proportion of papers of each faculty associated with each of the topics and drew a relationship between researchers and their most published topics. This will allow HDSI to personalize recommendations of faculty candidates to their industry partner’s particular job.

• Group members: Du Xiang

AI in Healthcare, Deep Reinforcement Learning, Trustworthy Machine Learning

Improving robustness in deep fusion modeling against adversarial attacks.

• Group members: Ayush More, Amy Nguyen

Abstract: Autonomous vehicles rely heavily on deep fusion modeling, which utilize multiple inputs for its inferences and decision making. By using the data from these inputs, the deep fusion model benefits from shared information, which is primarily associated with robustness as these input sources can face different levels of corruption. Thus, it is highly important that the deep fusion models used in autonomous vehicles are robust to corruption, especially to input sources that are weighted more heavily in different conditions. We explore a different approach in training the robustness for a deep fusion model through adversarial training. We fine-tune the model on adversarial examples and evaluate its robustness against single source noise and other forms of corruption. Our experimental results show that adversarial training was effective in improving the robustness of a deep fusion model object detector against adversarial noise and Gaussian noise while maintaining performance on clean data. The results also highlighted the lack of robustness of models that are not trained to handle adversarial examples. We believe that this is relevant given the risks that autonomous vehicles pose to pedestrians - it is important that we ensure the inferences and decisions made by the model are robust against corruption, especially if it is intentional from outside threats.

Healthcare: Adversarial Defense In Medical Deep Learning Systems

• Group members: Rakesh Senthilvelan, Madeline Tjoa

Abstract: In order to combat against such adversarial instances, there needs to be robust training done with these models in order to best protect against the methods that these attacks use on deep learning systems. In the scope of this paper, we will be looking into the methods of fast gradient signed method and projected gradient descent, two methods used in adversarial attacks to maximize loss functions and cause the affected system to make opposing predictions, in order to train our models against them and allow for stronger accuracy when faced with adversarial examples.

Satellite image analysis

Ml for finance, ml for healthcare, fair ml, ml for science, actionable recourse.

• Group members: Shweta Kumar, Trevor Tuttle, Takashi Yabuta, Mizuki Kadowaki, Jeffrey Feng

Abstract: In American society today there is a constant encouraged reliance on credit, despite it not being available to everyone as a legal right. Currently, there are countless evaluation methods of an individual's creditworthiness in practice. In an effort to regulate the selection criteria of different financial institutions, the Equal Credit Opportunity Act (ECOA) requires that applicants denied a loan are entitled to an Adverse Action notice, a statement from the creditor explaining the reason for the denial. However, these adverse action notices are frequently unactionable and ineffective in providing feedback to give an individual recourse, which is the ability to act up on a reason for denial to raise one’s odds of getting accepted for a loan. In our project, we will be exploring whether it is possible to create an interactive interface to personalize adverse action notices in alignment with personal preferences for individuals to gain recourse.

Social media; online communities; text analysis; ethics

Finding commonalities in misinformative articles across topics.

• Group members: Hwang Yu, Maximilian Halvax, Lucas Nguyen

Abstract: In order to combat the large scale distribution of misinformation online, We wanted to develop a way to flag news articles that are misinformative and could potentially mislead the general public. In addition to flagging news articles, we also wanted to find commonalities between the misinformation that we found. Were some topics in specific containing more misleading information than others? How much overlap do these articles have when we break their content down into TF IDF and see what words carry the most importance when put into various models detecting misinformation. We wanted to narrow down our models to be trained on four different topics: economics, politics, science, and general which is a dataset encompassing the three previous topics. We Found that general included the most overlap overall, while the topics themselves, while mostly different than the other specific topics, had certain models that still put emphasis on similar words, indicating a possible pattern of misinformative language in these articles. We believe, from these results, that we can find a pattern that could direct further investigation into how misinformation is written and distributed online.

The Effect of Twitter Cancel Culture on the Music Industry

• Group members: Peter Wu, Nikitha Gopal, Abigail Velasquez

Abstract: Musicians often trend on social media for various reasons but in recent years, there has been a rise in musicians being “canceled” for committing offensive or socially unacceptable behavior. Due to the wide accessibility of social media, the masses are able to hold accountable musicians for their actions through “cancel culture”, a form of modern ostracism. Twitter has become a well-known platform for “cancel culture” as users can easily spread hashtags and see what’s trending, which also has the potential to facilitate the spread of toxicity. We analyze how public sentiment towards canceled musicians on Twitter changes in respect to the type of issue they were canceled for, their background, and the strength of their parasocial relationship with their fans. Through our research, we aim to determine whether “cancel culture” leads to an increase in toxicity and negative sentiment towards a canceled individual.

Analyzing single cell multimodality data via (coupled) autoencoder neural networks

Coupled autoencoders for single-cell data analysis.

• Group members: Alex Nguyen, Brian Vi

Abstract: Historically, analysis on single-cell data has been difficult to perform, due to data collection methods often resulting in the destruction of the cell in the process of collecting information. However, an ongoing endeavor of biological data science has recently been to analyze different modalities, or forms, of the genetic information within a cell. Doing so will allow modern medicine a greater understanding of cellular functions and how cells work in the context of illnesses. The information collected on the three modalities of DNA, RNA, and protein can be done safely and because it is known that they are same information in different forms, analysis done on them can be extrapolated understand the cell as a whole. Previous research has been conducted by Gala, R., Budzillo, A., Baftizadeh, F. et al. to capture gene expression in neuron cells with a neural network called a coupled autoencoder. This autoencoder framework is able to reconstruct the inputs, allowing the prediction of one input to another, as well as align the multiple inputs in the same low dimensional representation. In our paper, we build upon this coupled autoencoder on a data set of cells taken from several sites of the human body, predicting from RNA information to protein. We find that the autoencoder is able to adequately cluster the cell types in its lower dimensional representation, as well as perform decently at the prediction task. We show that the autoencoder is a powerful tool for analyzing single-cell data analysis and may prove to be a valuable asset in single-cell data analysis.

Machine Learning, Natural Language Processing

On evaluating the robustness of language models with tuning.

• Group members: Lechuan Wang, Colin Wang, Yutong Luo

Abstract: Prompt tuning and prefix tuning are two effective mechanisms to leverage frozen language models to perform downstream tasks. Robustness reflects models’ resilience of output under a change or noise in the input. In this project, we analyze the robustness of natural language models using various tuning methods with respect to a domain shift (i.e. training on a domain but evaluating on out-of-domain data). We apply both prompt tuning and prefix tuning on T5 models for reading comprehension (i.e. question-answering) and GPT-2 models for table-to-text generation.

Activity Based Travel Models and Feature Selection

A tree-based model for activity based travel models and feature selection.

• Group members: Lisa Kuwahara, Ruiqin Li, Sophia Lau

Abstract: In a previous study, Deloitte Consulting LLP developed a method of creating city simulations through cellular location and geospatial data. Using these simulations of human activity and traffic patterns, better decisions can be made regarding modes of transportation or road construction. However, the current commonly used method of estimating transportation mode choice is a utility model that involves many features and coefficients that may not necessarily be important but still make the model more complex. Instead, we used a tree-based approach - in particular, XGBoost - to identify just the features that are important for determining mode choice so that we can create a model that is simpler, robust, and easily deployable, in addition to performing better than the original utility model on both the full dataset and population subsets.

Explainable AI, Causal Inference

Explainable ai.

• Group members: Jerry Chan, Apoorv Pochiraju, Zhendong Wang, Yujie Zhang

Abstract: Nowadays, the algorithmic decision-making system has been very common in people’s daily lives. Gradually, some algorithms become too complex for humans to interpret, such as some black-box machine learning models and deep neural networks. In order to assess the fairness of the models and make them better tools for different parties, we need explainable AI (XAI) to uncover the reasoning behind the predictions made by those black-box models. In our project, we will be focusing on using different techniques from causal inferences and explainable AI to interpret various classification models across various domains. In particular, we are interested in three domains - healthcare, finance, and the housing market. Within each domain, we are going to train four binary classification models first, and we have four goals in general: 1) Explaining black-box models both globally and locally with various XAI methods. 2) Assessing the fairness of each learning algorithm with regard to different sensitive attributes; 3) Generating recourse for individuals - a set of minimal actions to change the prediction of those black-box models. 4) Evaluating the explanations from those XAI methods using domain knowledge.

AutoML Platforms

Deep learning transformer models for feature type inference.

• Group members: Andrew Shen, Tanveer Mittal

Abstract: The first step AutoML software must take after loading in the data is to identify the feature types of individual columns in input data. This information then allows the software to understand the data and then preprocess it to allow machine learning algorithms to run on it. Project Sortinghat of the ADA lab at UCSD frames this task of Feature Type Inference as a machine learning multiclass classification problem. Machine learning models defined in the original SortingHat feature type inference paper use 3 sets of features as input. 1. The name of the given column 2. 5 not null sample values 3. Descriptive numeric features about the column The textual features are easy to access, however the descriptive statistics previous models rely on require a full pass through the data which make preprocessing less scalable. Our goal is to produce models that may rely less on these statistics by better leveraging the textual features. As an extension of Project SortingHat, we experimented with deep learning transformer models and varying the sample sizes used by random forest models. We found that our transformer models achieved state of the art results on this task which outperform all existing tools and ML models that have been benchmarked against SortingHat's ML Data Prep Zoo. Our best model used a pretrained Bidirectional Encoder Representations Transformer(BERT) language model to produce word embeddings which are then processed by a Convolutional Neural Network(CNN) model. As a result of this project, we have published 2 BERT CNN models using the PyTorch Hub api. This is to allow software engineers to easily integrate our models or train similar ones for use in AutoML platforms or other automated data preparation applications. Our best model uses all the features defined above, while the other only uses column names and sample values while offering comparable performance and much better scalability for all input data.

Exploring Noise in Data: Applications to ML Models

• Group members: Cheolmin Hwang, Amelia Kawasaki, Robert Dunn

Abstract: In machine learning, models are commonly built in such a way to avoid what is known as overfitting. As it is generally understood, overfitting is when a model is fit exactly to the training data causing the model to have poor performance on new examples. This means that overfit models tend to have poor accuracy on unseen data because the model is fit exactly to the training data. Therefore, in order to generalize to all examples of data and not only the examples found in a given training set, models are built with certain techniques to avoid fitting the data exactly. However, it can be found that overfitting does not always work in this way that one might expect as will be shown by fitting models with a given level of noisiness. Specifically, it is seen that some models fit exactly to data with high levels of noise still produce results with high accuracy whereas others are more prone to overfitting.

Group Testing for Optimizing COVID-19 Testing

Covid-19 group testing optimization strategies.

• Group members: Mengfan Chen, Jeffrey Chu, Vincent Lee, Ethan Dinh-Luong

Abstract: The COVID-19 pandemic that has persisted for more than two years has been combated by efficient testing strategies that reliably identifies positive individuals to slow the spread of the pandemic. Opposed to other pooling strategies within the domain, the methods described in this paper prioritize true negative samples over overall accuracy. In the Monte Carlo simulations, both nonadaptive and adaptive testing strategies with random pool sampling resulted in high accuracy approaching at least 95% with varying pooling sizes and population sizes to decrease the number of tests given. A split tensor rank 2 method attempts to identify all infected samples within 961 samples, converging the number of tests to 99 as the prevalence of infection converges to 1%.

Causal Discovery

Patterns of fairness in machine learning.

• Group members: Daniel Tong, Anne Xu, Praveen Nair

Abstract: Machine learning tools are increasingly used for decision-making in contexts that have crucial ramifications. However, a growing body of research has established that machine learning models are not immune to bias, especially on protected characteristics. This had led to efforts to create mathematical definitions of fairness that could be used to estimate whether, given a prediction task and a certain protected attribute, an algorithm is being fair to members of all classes. But just like how philosophical definitions of fairness can vary widely, mathematical definitions of fairness vary as well, and fairness conditions can in fact be mutually exclusive. In addition, the choice of model to use to optimize fairness is also a difficult decision we have little intuition for. Consequently, our capstone project centers around an empirical analysis for studying the relationships between machine learning models, datasets, and various fairness metrics. We produce a 3-dimensional matrix of the performance of a certain machine learning model, for a certain definition of fairness, for a certain given dataset. Using this matrix on a sample of 8 datasets, 7 classification models, and 9 fairness metrics, we discover empirical relationships between model type and performance on specific metrics, in addition to correlations between metric values across different dataset-model pairs. We also offer a website and command-line interface for users to perform this experimentation on their own datasets.

Causal Effects of Socioeconomic and Political Factors on Life Expectancy in 166 Different Countries

• Group members: Adam Kreitzman, Maxwell Levitt, Emily Ramond

Abstract: This project examines causal relationships between various socioeconomic variables and life expectancy outcomes in 166 different countries, with the ability to account for new, unseen data and variables with an intuitive data pipeline process with detailed instructions and the PC algorithm with updated code to account for missingness in data. With access to this model and pipeline, we hope that questions such as “do authoritarian countries have a direct relation to life expectancy?” or “how does women in government affect perceived notion of social support?” will now be able to be answered and understood. Through our own analysis, we were able to find intriguing results, such as a higher Perception of Corruption is distinctly related to a lower Life Ladder score. We also found that higher quality of life perceptions is related to lower economic inequality. These results aim to educate not only the general public, but government officials as well.

Time series analysis in health

Time series analysis on the effect of light exposure on sleep quality.

• Group members: Shubham Kaushal, Yuxiang Hu, Alex Liu

Abstract: The increase of artificial light exposure through the increased prevalence of technology has an affect on the sleep cycle and circadian rhythm of humans. The goal of this project is to determine how different colors and intensities of light exposure prior to sleep affects the quality of sleep through the classification of time series data.

Sleep Stage Classification for Patients With Sleep Apnea

• Group members: Kevin Chin, Yilan Guo, Shaheen Daneshvar

Abstract: Sleeping is not uniform and consists of four stages: N1, N2, N3, and REM sleep. The analysis of sleep stages is essential for understanding and diagnosing sleep-related diseases, such as insomnia, narcolepsy, and sleep apnea; however, sleep stage classification often does not generalize to patients with sleep apnea. The goal of our project is to build a sleep stage classifier specifically for people with sleep apnea and understand how it differs from the normal sleep stage. We will then explore whether or not the inclusion and featurization of ECG data will improve the performance of our model.

Environmental health exposures & pollution modeling & land-use change dynamics

Supervised classification approach to wildfire mapping in northern california.

• Group members: Alice Lu, Oscar Jimenez, Anthony Chi, Jaskaranpal Singh

Abstract: Burn severity maps are an important tool for understanding fire damage and managing forest recovery. We have identified several issues with current mapping methods used by federal agencies that affect the completeness, consistency, and efficiency of their burn severity maps. In order to address these issues, we demonstrate the use of machine learning as an alternative to traditional methods of producing severity maps, which rely on in-situ data and spectral indices derived from image algebra. We have trained several supervised classifiers on sample data collected from 17 wildfires across Northern California and evaluate their performance at mapping fire severity.

Network Performance Classification

Network signal anomaly detection.

• Group members: Laura Diao, Benjamin Sam, Jenna Yang

Abstract: Network degradation occurs in many forms, and our project will focus on two common factors: packet loss and latency. Packet loss occurs when one or more data packets transmitted across a computer network fail to reach their destination. Latency can be defined as a measure of delay for data to transmit across a network. For internet users, high rates of packet loss and significant latency can manifest in jitter or lag, which are indicators of overall poor network performance as perceived by the end user. Thus, when issues arise in these two factors, it would be beneficial for internet service providers to know exactly when the user is experiencing problems in real time. In real world scenarios, situations or environments such as poor port quality, overloaded ports, network congestion and more can impact overall network performance. In order to detect some of these issues in network transmission data, we built an anomaly detection system that predicts the estimated packet loss and latency of a connection and detects whether there is a significant degradation of network quality for the duration of the connection.

Real Time Anomaly Detection in Networks

• Group members: Justin Harsono, Charlie Tran, Tatum Maston

Abstract: Internet companies are expected to deliver the speed their customer has paid for. However, for various reasons such as congestion or connectivity issues, it is inevitable for one to perceive degradations in network quality. To still ensure the customer is satisfied, certain monitoring systems must be built to inspect the quality of the connection. Our goal is to build a model that would be able to detect, in real time, these regions of networks degradations, so that an appropriate recovery can be enacted to offset these degradations. Our solution is a combination of two anomaly detection methods that successfully detects shifts in the data, based on a rolling window of data it has seen.

System Usage Reporting

Intel telemetry: data collection & time-series prediction of app usage.

• Group members: Srikar Prayaga, Andrew Chin, Arjun Sawhney

Abstract: Despite advancements in hardware technology, PC users continue to face frustrating app launch times, especially on lower end Windows machines. The desktop experience differs vastly from the instantaneous app launches and optimized experience we have come to expect even from low end smartphones. We propose a solution to preemptively run Windows apps in the background based on the app usage patterns of the user. Our solution is two-step. First, we built telemetry collector modules in C/C++ to collect real-world app usage data from two of our personal Windows 10 devices. Next, we developed neural network models, trained on the collected data, to predict app usage times and corresponding launch sequences in python. We achieved impressive results on selected evaluation metrics across different user profiles.

Predicting Application Use to Reduce User Wait Time

• Group members: Sasami Scott, Timothy Tran, Andy Do

Abstract: Our goal for this project was to lower the user wait time when loading programs by predicting the next used application. In order to obtain the needed data, we created data collection libraries. Using this data, we created a Hidden Markov Model (HMM) and a Long Short-Term Memory (LSTM) model, but the latter proved to be better. Using LSTM, we can predict the application use time and expand this concept to more applications. We created multiple LSTM models with varying results, but ultimately chose a model that we think had potential. We decided on using the model that reported a 90% accuracy.

INTELlinext: A Fully Integrated LSTM and HMM-Based Solution for Next-App Prediction With Intel SUR SDK Data Collection

• Group members: Jared Thach, Hiroki Hoshida, Cyril Gorlla

Abstract: As the power of modern computing devices increases, so too do user expectations for them. Despite advancements in technology, computer users are often faced with the dreaded spinning icon waiting for an application to load. Building upon our previous work developing data collectors with the Intel System Usage Reporting (SUR) SDK, we introduce INTELlinext, a comprehensive solution for next-app prediction for application preload to improve perceived system fluidity. We develop a Hidden Markov Model (HMM) for prediction of the k most likely next apps, achieving an accuracy of 64% when k = 3. We then implement a long short-term memory (LSTM) model to predict the total duration that applications will be used. After hyperparameter optimization leading to an optimal lookback value of 5 previous applications, we are able to predict the usage time of a given application with a mean absolute error of ~45 seconds. Our work constitutes a promising comprehensive application preload solution with data collection based on the Intel SUR SDK and prediction with machine learning.

10 Unique Data Science Capstone Project Ideas

A capstone project is a culminating assignment that allows students to demonstrate the skills and knowledge they’ve acquired throughout their degree program. For data science students, it’s a chance to tackle a substantial real-world data problem.

If you’re short on time, here’s a quick answer to your question: Some great data science capstone ideas include analyzing health trends, building a predictive movie recommendation system, optimizing traffic patterns, forecasting cryptocurrency prices, and more .

In this comprehensive guide, we will explore 10 unique capstone project ideas for data science students. We’ll overview potential data sources, analysis methods, and practical applications for each idea.

Whether you want to work with social media datasets, geospatial data, or anything in between, you’re sure to find an interesting capstone topic.

Project Idea #1: Analyzing Health Trends

When it comes to data science capstone projects, analyzing health trends is an intriguing idea that can have a significant impact on public health. By leveraging data from various sources, data scientists can uncover valuable insights that can help improve healthcare outcomes and inform policy decisions.

Data Sources

There are several data sources that can be used to analyze health trends. One of the most common sources is electronic health records (EHRs), which contain a wealth of information about patient demographics, medical history, and treatment outcomes.

Other sources include health surveys, wearable devices, social media, and even environmental data.

Analysis Approaches

When analyzing health trends, data scientists can employ a variety of analysis approaches. Descriptive analysis can provide a snapshot of current health trends, such as the prevalence of certain diseases or the distribution of risk factors.

Predictive analysis can be used to forecast future health outcomes, such as predicting disease outbreaks or identifying individuals at high risk for certain conditions. Machine learning algorithms can be trained to identify patterns and make accurate predictions based on large datasets.

Applications

The applications of analyzing health trends are vast and far-reaching. By understanding patterns and trends in health data, policymakers can make informed decisions about resource allocation and public health initiatives.

Healthcare providers can use these insights to develop personalized treatment plans and interventions. Researchers can uncover new insights into disease progression and identify potential targets for intervention.

Ultimately, analyzing health trends has the potential to improve overall population health and reduce healthcare costs.

Project Idea #2: Movie Recommendation System

When developing a movie recommendation system, there are several data sources that can be used to gather information about movies and user preferences. One popular data source is the MovieLens dataset, which contains a large collection of movie ratings provided by users.

Another source is IMDb, a trusted website that provides comprehensive information about movies, including user ratings and reviews. Additionally, streaming platforms like Netflix and Amazon Prime also provide access to user ratings and viewing history, which can be valuable for building an accurate recommendation system.

There are several analysis approaches that can be employed to build a movie recommendation system. One common approach is collaborative filtering, which uses user ratings and preferences to identify patterns and make recommendations based on similar users’ preferences.

Another approach is content-based filtering, which analyzes the characteristics of movies (such as genre, director, and actors) to recommend similar movies to users. Hybrid approaches that combine both collaborative and content-based filtering techniques are also popular, as they can provide more accurate and diverse recommendations.

A movie recommendation system has numerous applications in the entertainment industry. One application is to enhance the user experience on streaming platforms by providing personalized movie recommendations based on individual preferences.

This can help users discover new movies they might enjoy and improve overall satisfaction with the platform. Additionally, movie recommendation systems can be used by movie production companies to analyze user preferences and trends, aiding in the decision-making process for creating new movies.

Finally, movie recommendation systems can also be utilized by movie critics and reviewers to identify movies that are likely to be well-received by audiences.

For more information on movie recommendation systems, you can visit https://www.kaggle.com/rounakbanik/movie-recommender-systems or https://www.researchgate.net/publication/221364567_A_new_movie_recommendation_system_for_large-scale_data .

Project Idea #3: Optimizing Traffic Patterns

When it comes to optimizing traffic patterns, there are several data sources that can be utilized. One of the most prominent sources is real-time traffic data collected from various sources such as GPS devices, traffic cameras, and mobile applications.

This data provides valuable insights into the current traffic conditions, including congestion, accidents, and road closures. Additionally, historical traffic data can also be used to identify recurring patterns and trends in traffic flow.

Other data sources that can be used include weather data, which can help in understanding how weather conditions impact traffic patterns, and social media data, which can provide information about events or incidents that may affect traffic.

Optimizing traffic patterns requires the use of advanced data analysis techniques. One approach is to use machine learning algorithms to predict traffic patterns based on historical and real-time data.

These algorithms can analyze various factors such as time of day, day of the week, weather conditions, and events to predict traffic congestion and suggest alternative routes.

Another approach is to use network analysis to identify bottlenecks and areas of congestion in the road network. By analyzing the flow of traffic and identifying areas where traffic slows down or comes to a halt, transportation authorities can make informed decisions on how to optimize traffic flow.

The optimization of traffic patterns has numerous applications and benefits. One of the main benefits is the reduction of traffic congestion, which can lead to significant time and fuel savings for commuters.

By optimizing traffic patterns, transportation authorities can also improve road safety by reducing the likelihood of accidents caused by congestion.

Additionally, optimizing traffic patterns can have positive environmental impacts by reducing greenhouse gas emissions. By minimizing the time spent idling in traffic, vehicles can operate more efficiently and emit fewer pollutants.

Furthermore, optimizing traffic patterns can have economic benefits by improving the flow of goods and services. Efficient traffic patterns can reduce delivery times and increase productivity for businesses.

Project Idea #4: Forecasting Cryptocurrency Prices

With the growing popularity of cryptocurrencies like Bitcoin and Ethereum, forecasting their prices has become an exciting and challenging task for data scientists. This project idea involves using historical data to predict future price movements and trends in the cryptocurrency market.

When working on this project, data scientists can gather cryptocurrency price data from various sources such as cryptocurrency exchanges, financial websites, or APIs. Websites like CoinMarketCap (https://coinmarketcap.com/) provide comprehensive data on various cryptocurrencies, including historical price data.

Additionally, platforms like CryptoCompare (https://www.cryptocompare.com/) offer real-time and historical data for different cryptocurrencies.

To forecast cryptocurrency prices, data scientists can employ various analysis approaches. Some common techniques include:

• Time Series Analysis: This approach involves analyzing historical price data to identify patterns, trends, and seasonality in cryptocurrency prices. Techniques like moving averages, autoregressive integrated moving average (ARIMA), or exponential smoothing can be used to make predictions.
• Machine Learning: Machine learning algorithms, such as random forests, support vector machines, or neural networks, can be trained on historical cryptocurrency data to predict future price movements. These algorithms can consider multiple variables, such as trading volume, market sentiment, or external factors, to make accurate predictions.
• Sentiment Analysis: This approach involves analyzing social media sentiment and news articles related to cryptocurrencies to gauge market sentiment. By considering the collective sentiment, data scientists can predict how positive or negative sentiment can impact cryptocurrency prices.

Forecasting cryptocurrency prices can have several practical applications:

• Investment Decision Making: Accurate price forecasts can help investors make informed decisions when buying or selling cryptocurrencies. By considering the predicted price movements, investors can optimize their investment strategies and potentially maximize their returns.
• Trading Strategies: Traders can use price forecasts to develop trading strategies, such as trend following or mean reversion. By leveraging predicted price movements, traders can make profitable trades in the volatile cryptocurrency market.
• Risk Management: Cryptocurrency price forecasts can help individuals and organizations manage their risk exposure. By understanding potential price fluctuations, risk management strategies can be implemented to mitigate losses.

Project Idea #5: Predicting Flight Delays

One interesting and practical data science capstone project idea is to create a model that can predict flight delays. Flight delays can cause a lot of inconvenience for passengers and can have a significant impact on travel plans.

By developing a predictive model, airlines and travelers can be better prepared for potential delays and take appropriate actions.

To create a flight delay prediction model, you would need to gather relevant data from various sources. Some potential data sources include:

• Flight data from airlines or aviation organizations
• Weather data from meteorological agencies
• Historical flight delay data from airports

By combining these different data sources, you can build a comprehensive dataset that captures the factors contributing to flight delays.

Once you have collected the necessary data, you can employ different analysis approaches to predict flight delays. Some common approaches include:

• Machine learning algorithms such as decision trees, random forests, or neural networks
• Time series analysis to identify patterns and trends in flight delay data
• Feature engineering to extract relevant features from the dataset

By applying these analysis techniques, you can develop a model that can accurately predict flight delays based on the available data.

The applications of a flight delay prediction model are numerous. Airlines can use the model to optimize their operations, improve scheduling, and minimize disruptions caused by delays. Travelers can benefit from the model by being alerted in advance about potential delays and making necessary adjustments to their travel plans.

Additionally, airports can use the model to improve resource allocation and manage passenger flow during periods of high delay probability. Overall, a flight delay prediction model can significantly enhance the efficiency and customer satisfaction in the aviation industry.

Project Idea #6: Fighting Fake News

With the rise of social media and the easy access to information, the spread of fake news has become a significant concern. Data science can play a crucial role in combating this issue by developing innovative solutions.

Here are some aspects to consider when working on a project that aims to fight fake news.

When it comes to fighting fake news, having reliable data sources is essential. There are several trustworthy platforms that provide access to credible news articles and fact-checking databases. Websites like Snopes and FactCheck.org are good starting points for obtaining accurate information.

Additionally, social media platforms such as Twitter and Facebook can be valuable sources for analyzing the spread of misinformation.

One approach to analyzing fake news is by utilizing natural language processing (NLP) techniques. NLP can help identify patterns and linguistic cues that indicate the presence of misleading information.

Sentiment analysis can also be employed to determine the emotional tone of news articles or social media posts, which can be an indicator of potential bias or misinformation.

Another approach is network analysis, which focuses on understanding how information spreads through social networks. By analyzing the connections between users and the content they share, it becomes possible to identify patterns of misinformation dissemination.

Network analysis can also help in identifying influential sources and detecting coordinated efforts to spread fake news.

The applications of a project aiming to fight fake news are numerous. One possible application is the development of a browser extension or a mobile application that provides users with real-time fact-checking information.

This tool could flag potentially misleading articles or social media posts and provide users with accurate information to help them make informed decisions.

Another application could be the creation of an algorithm that automatically identifies fake news articles and separates them from reliable sources. This algorithm could be integrated into news aggregation platforms to help users distinguish between credible and non-credible information.

Project Idea #7: Analyzing Social Media Sentiment

Social media platforms have become a treasure trove of valuable data for businesses and researchers alike. When analyzing social media sentiment, there are several data sources that can be tapped into. The most popular ones include:

• Twitter: With its vast user base and real-time nature, Twitter is often the go-to platform for sentiment analysis. Researchers can gather tweets containing specific keywords or hashtags to analyze the sentiment of a particular topic.
• Facebook: Facebook offers rich data for sentiment analysis, including posts, comments, and reactions. Analyzing the sentiment of Facebook posts can provide valuable insights into user opinions and preferences.
• Instagram: Instagram’s visual nature makes it an interesting platform for sentiment analysis. By analyzing the comments and captions on Instagram posts, researchers can gain insights into the sentiment associated with different images or topics.
• Reddit: Reddit is a popular platform for discussions on various topics. By analyzing the sentiment of comments and posts on specific subreddits, researchers can gain insights into the sentiment of different communities.

These are just a few examples of the data sources that can be used for analyzing social media sentiment. Depending on the research goals, other platforms such as LinkedIn, YouTube, and TikTok can also be explored.

When it comes to analyzing social media sentiment, there are various approaches that can be employed. Some commonly used analysis techniques include:

• Lexicon-based analysis: This approach involves using predefined sentiment lexicons to assign sentiment scores to words or phrases in social media posts. By aggregating these scores, researchers can determine the overall sentiment of a post or a collection of posts.
• Machine learning: Machine learning algorithms can be trained to classify social media posts into positive, negative, or neutral sentiment categories. These algorithms learn from labeled data and can make predictions on new, unlabeled data.
• Deep learning: Deep learning techniques, such as recurrent neural networks (RNNs) or convolutional neural networks (CNNs), can be used to capture the complex patterns and dependencies in social media data. These models can learn to extract sentiment information from textual or visual content.

It is important to note that the choice of analysis approach depends on the specific research objectives, available resources, and the nature of the social media data being analyzed.

Analyzing social media sentiment has a wide range of applications across different industries. Here are a few examples:

• Brand reputation management: By analyzing social media sentiment, businesses can monitor and manage their brand reputation. They can identify potential issues, respond to customer feedback, and take proactive measures to maintain a positive image.
• Market research: Social media sentiment analysis can provide valuable insights into consumer opinions and preferences. Businesses can use this information to understand market trends, identify customer needs, and develop targeted marketing strategies.
• Customer feedback analysis: Social media sentiment analysis can help businesses understand customer satisfaction levels and identify areas for improvement. By analyzing sentiment in customer feedback, companies can make data-driven decisions to enhance their products or services.
• Public opinion analysis: Researchers can analyze social media sentiment to study public opinion on various topics, such as political events, social issues, or product launches. This information can be used to understand public sentiment, predict trends, and inform decision-making.

These are just a few examples of how analyzing social media sentiment can be applied in real-world scenarios. The insights gained from sentiment analysis can help businesses and researchers make informed decisions, improve customer experience, and drive innovation.

Project Idea #8: Improving Online Ad Targeting

Improving online ad targeting involves analyzing various data sources to gain insights into users’ preferences and behaviors. These data sources may include:

• Website analytics: Gathering data from websites to understand user engagement, page views, and click-through rates.
• Demographic data: Utilizing information such as age, gender, location, and income to create targeted ad campaigns.
• Social media data: Extracting data from platforms like Facebook, Twitter, and Instagram to understand users’ interests and online behavior.
• Search engine data: Analyzing search queries and user behavior on search engines to identify intent and preferences.

By combining and analyzing these diverse data sources, data scientists can gain a comprehensive understanding of users and their ad preferences.

To improve online ad targeting, data scientists can employ various analysis approaches:

• Segmentation analysis: Dividing users into distinct groups based on shared characteristics and preferences.
• Collaborative filtering: Recommending ads based on users with similar preferences and behaviors.
• Predictive modeling: Developing algorithms to predict users’ likelihood of engaging with specific ads.
• Machine learning: Utilizing algorithms that can continuously learn from user interactions to optimize ad targeting.

These analysis approaches help data scientists uncover patterns and insights that can enhance the effectiveness of online ad campaigns.

Improved online ad targeting has numerous applications:

• Increased ad revenue: By delivering more relevant ads to users, advertisers can expect higher click-through rates and conversions.
• Better user experience: Users are more likely to engage with ads that align with their interests, leading to a more positive browsing experience.
• Reduced ad fatigue: By targeting ads more effectively, users are less likely to feel overwhelmed by irrelevant or repetitive advertisements.
• Maximized ad budget: Advertisers can optimize their budget by focusing on the most promising target audiences.

Project Idea #9: Enhancing Customer Segmentation

Enhancing customer segmentation involves gathering relevant data from various sources to gain insights into customer behavior, preferences, and demographics. Some common data sources include:

• Customer transaction data
• Customer surveys and feedback
• Social media data
• Website analytics
• Customer support interactions

By combining data from these sources, businesses can create a comprehensive profile of their customers and identify patterns and trends that will help in improving their segmentation strategies.

There are several analysis approaches that can be used to enhance customer segmentation:

• Clustering: Using clustering algorithms to group customers based on similar characteristics or behaviors.
• Classification: Building predictive models to assign customers to different segments based on their attributes.
• Association Rule Mining: Identifying relationships and patterns in customer data to uncover hidden insights.
• Sentiment Analysis: Analyzing customer feedback and social media data to understand customer sentiment and preferences.

These analysis approaches can be used individually or in combination to enhance customer segmentation and create more targeted marketing strategies.

Enhancing customer segmentation can have numerous applications across industries:

• Personalized marketing campaigns: By understanding customer preferences and behaviors, businesses can tailor their marketing messages to individual customers, increasing the likelihood of engagement and conversion.
• Product recommendations: By segmenting customers based on their purchase history and preferences, businesses can provide personalized product recommendations, leading to higher customer satisfaction and sales.
• Customer retention: By identifying at-risk customers and understanding their needs, businesses can implement targeted retention strategies to reduce churn and improve customer loyalty.
• Market segmentation: By identifying distinct customer segments, businesses can develop tailored product offerings and marketing strategies for each segment, maximizing the effectiveness of their marketing efforts.

Project Idea #10: Building a Chatbot

A chatbot is a computer program that uses artificial intelligence to simulate human conversation. It can interact with users in a natural language through text or voice. Building a chatbot can be an exciting and challenging data science capstone project.

It requires a combination of natural language processing, machine learning, and programming skills.

When building a chatbot, data sources play a crucial role in training and improving its performance. There are various data sources that can be used:

• Chat logs: Analyzing existing chat logs can help in understanding common user queries, responses, and patterns. This data can be used to train the chatbot on how to respond to different types of questions and scenarios.
• Knowledge bases: Integrating a knowledge base can provide the chatbot with a wide range of information and facts. This can be useful in answering specific questions or providing detailed explanations on certain topics.
• APIs: Utilizing APIs from different platforms can enhance the chatbot’s capabilities. For example, integrating a weather API can allow the chatbot to provide real-time weather information based on user queries.

There are several analysis approaches that can be used to build an efficient and effective chatbot:

• Natural Language Processing (NLP): NLP techniques enable the chatbot to understand and interpret user queries. This involves tasks such as tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis.
• Intent recognition: Identifying the intent behind user queries is crucial for providing accurate responses. Machine learning algorithms can be trained to classify user intents based on the input text.
• Contextual understanding: Chatbots need to understand the context of the conversation to provide relevant and meaningful responses. Techniques such as sequence-to-sequence models or attention mechanisms can be used to capture contextual information.

Chatbots have a wide range of applications in various industries:

• Customer support: Chatbots can be used to handle customer queries and provide instant support. They can assist with common troubleshooting issues, answer frequently asked questions, and escalate complex queries to human agents when necessary.
• E-commerce: Chatbots can enhance the shopping experience by assisting users in finding products, providing recommendations, and answering product-related queries.
• Healthcare: Chatbots can be deployed in healthcare settings to provide preliminary medical advice, answer general health-related questions, and assist with appointment scheduling.

Building a chatbot as a data science capstone project not only showcases your technical skills but also allows you to explore the exciting field of artificial intelligence and natural language processing.

It can be a great opportunity to create a practical and useful tool that can benefit users in various domains.

Completing an in-depth capstone project is the perfect way for data science students to demonstrate their technical skills and business acumen. This guide outlined 10 unique project ideas spanning industries like healthcare, transportation, finance, and more.

By identifying the ideal data sources, analysis techniques, and practical applications for their chosen project, students can produce an impressive capstone that solves real-world problems and showcases their abilities.

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Top 10 Data Science Project Ideas in 2024

Join over 2 million students who advanced their careers with 365 Data Science. Learn from instructors who have worked at Meta, Spotify, Google, IKEA, Netflix, and Coca-Cola and master Python, SQL, Excel, machine learning, data analysis, AI fundamentals, and more.

Data science is a practical field. You need various hands-on skills to stand out and advance your career. One of the best ways to obtain them is by building end-to-end data science projects that solve complex problems using real-world datasets.

Not sure where to start?

In this article, we provide 10 case studies from finance, healthcare, marketing, manufacturing, and other industries. You can use them as inspiration and adapt them to the domain of your interest.

All projects involve real business cases. Each one starts with a brief description of the problem, followed by an outline of the methodology, then the expected output, and finally, a recommended dataset and a relevant research paper. Most of the datasets are available on Kaggle or can be web scraped.

If you wish to start a project without the trouble of selecting and locating resources, we've prepared a series of engaging and relevant projects on our platform. These projects offer valuable hands-on practice to test your skills.

You can also include them in your portfolio to demonstrate to potential employers your experience in tackling everyday job challenges. For more information, check out the projects page on our website.

Below, we present 10 data science project ideas with step-by-step solutions. But first, we’ll explain what the data science life cycle is and how to execute an end-to-end project. Continue reading to learn to how to recognize and use your resources to turn information into a data science project.

Top 10 Data Science Project Ideas: Table of Contents

The data science life cycle, hospital treatment pricing prediction, youtube comments analysis, illegal fishing classification.

• Bank Customer Segmentation

Dogecoin Cryptocurrency Prices Predictor with LSTM

Book recommendation system, gender detection and age prediction using deep learning, speech emotion recognition for customer satisfaction, traveling agency customer service chatbots, detection of metallic surface defects.

• Data Science Project Ideas: Next Steps\

End-to-end projects involve real-world problems which you solve using the 6 stages of the data science life cycle:

• Business understanding
• Data understanding
• Data preparation

Here’s how to execute a data science project from end to end in more detail.

First, you define the business questions, requirements, and performance measurement. After that, you collect data to answer these questions. Then come the cleaning and preparation processes to get the data ready for exploration and analysis. These are the understanding stages.

But we’re not done yet.

Next comes the data preparation process. It involves the preprocessing and engineering of the features to prepare for the modeling step. Once that’s done, you can train the models on the prepared data. Depending on the task you are working on, you can do one of two things:

• Deploy the model on a live server and integrate it into a mobile or web application; then, monitor it and iterate again if needed, or
• Build dashboards based on the insights extracted from the data and the modeling step.

That wraps up the data science life cycle. Before you start working, you need some ideas for a data science project.

For starters, select a domain you are interested in. You can choose one that fits your educational background or previous work experience. This will give you a head start as you will know the field.

After that, you need to explore the common problems in this domain and how data science can solve them. Finally, choose a case study and formulate the business questions. Only then can you apply the life cycle we discussed above.

Now, let’s get started with a few project ideas.

The increasing cost of healthcare services is a major concern, especially for patients in the US. However, if planned properly, it can be reduced significantly.

The purpose of this project is to predict hospital charges before admitting a patient. Data science projects like this one are a great addition to your portfolio, especially if you want to pursue a career in healthcare .

Project Description

This will allow people to compare the costs at different medical institutions and plan their finances accordingly in case of elective admissions. It will also enable insurance companies to predict how much a patient with a particular medical condition might claim after a hospitalization.

You can solve this project using predictive analysis . This type of advanced analytics allows us to make predictions about future outcomes based on historical data. Typically, it involves statistical modeling, data mining, and machine learning techniques. In this case, we estimate hospital treatment costs based on the patient’s clinical data at admission.

Methodology

• Collect the hospital package pricing dataset
• Explore and understand the data
• Clean the data
• Perform engineering and preprocessing to prepare for the modeling step
• Select the suitable predictive model and train it with the data
• Deploy the model on a live server and integrate it into a web application to predict the pricing in real time
• Monitor the model in production and iterate

Expected Output

There are two expected outputs from this project:

• Analytical dashboard with insights extracted from the data that can be delivered to hospital and insurance companies
• Deployed predictive model into production on a live server that can be integrated into a web or mobile application and predict treatment costs in real time

Suggest Dataset:

• Package Pricing at Mission Hospital

Research Paper:

• Predicting the Inpatient Hospital Cost Using Machine Learning

This following example is form the marketing and finance domain .

Sentiment analysis or opinion mining refers to the analysis of the attitudes, feedback, and emotions users express on social media and other online platforms. It involves the detection of patterns in natural language that allude to people’s attitudes toward certain products or topics.

YouTube is the second most popular website in the world. Its comments section is a great source of user opinions on various topics. There are many examples of how you can approach such a data science project.

Let’s explore one of them.

You can analyze YouTube comments with natural language processing techniques. Begin by scraping text data using the library YouTube-Comment-Scraper-Python. It fetches comments utilizing browser automation.

Then, apply natural processing and text processing techniques to extract features, analyze them, and find the answers to the business questions you posed. You can build a dashboard to present the insights.

• Define the business questions you want to answer
• Build a web scrapper to collect data
• Clean the scraped data
• Text preprocessing to extract features
• Exploratory data analysis to extract insights from the data
• Build dashboards to present the insights interactively

Dashboards with insights from the scraped data.

Suggested Data

• Most Liked Comments on YouTube
• Analysis and Classification of User Comments on YouTube Videos
• Sentiment Analysis on YouTube Comments: A Brief Study

Marine life has a significant impact on our planet, providing food, oxygen, and biodiversity. Unfortunately, 90% of the large fish are gone primarily as a result of overfishing . In addition, many major fisheries notice increases in illegal fishing, undermining the efforts to conserve and manage fish stocks.

Detecting fishing activities in the ocean is a crucial step in achieving sustainability. It’s also an excellent big data project to add to your portfolio.

Identifying whether a vessel is fishing illegally and where this activity is likely to occur is a major step in ending illegal, unreported, and unregulated (IUU) fishing. However, monitoring the oceans is costly, time-consuming, and logistically difficult.

To overcome these challenges, we must improve the ability to detect and predict illegal fishing. This can be done using classification machine learning models to recognize and trace illegal fishing activity by collecting and processing GPS data from ships, as well as other pieces of information. The classification algorithm can distinguish these ships by type, fishing gear, and fishing behaviors.

• Collect the fishing watch dataset
• Perform data exploration to understand it better
• Perform engineering to extract features from the data
• Train classification models to categorize the fishing activity
• Deploy the trained model on a live server and integrate it into a web application
• Finish by monitoring the model in production and iterating

Deployed model running in a live server and used within a web service or mobile application to predict illegal fishing in real time.

Suggested Dataset

• Global Fishing Watch datasets

Research Papers

• Fishing Activity Detection from AIS Data Using Autoencoders
• Predicting Illegal Fishing on the Patagonia Shelf from Oceanographic Seascapes

The competition in the banking sector is increasing. To improve their services and retain and attract clients, banking and non-bank institutions need to modernize their marketing and customer strategies through personalization.

There are various data science models that could aid these efforts. Here, we focus on customer segmentation analysis .

Customer or market segmentation helps develop more effective investment and personalization strategies with the available information about clients. This is the process of grouping customers based on common characteristics, such as demographics or behaviors. This substantially improves targeting.

In this project, we segment Indian bank customers using data from more than one million transactions. We extract valuable information from these clusters and build dashboards with the insights. The final outputs can be used to improve products and marketing strategies.

• Define the questions you would like to answer with the data
• Collect the customer dataset
• Perform exploratory data analysis to have a better understanding of the data
• Perform feature preprocessing
• Train clustering models to segment the data into a selected number of groups
• Conduct cluster analysis to extract insights
• Build dashboards with the insights

Dashboards with marketing insights extracted from the segmented customers.

• A Customer Segmentation Approach in Commercial Banks

Dogecoin became one of the most popularity cryptocurrencies in recent years. Its price peaked in 2021, and it’s been slowly decreasing in 2022. That’s the case with most cryptocurrencies in the current economic situation.

However, the constant fluctuations make it hard for a human being to predict with accuracy the future prices. As such, automated algorithms are commonly used in finance .

This is an extremely valuable data science project for your resume if you want to pursue a career in this domain. If that’s your goal, you also need to learn how to use Python for Finance .

In this section, we discuss a time series forecasting project, commonly encountered in the financial sector .

A time series is a sequence of data points distributed over a time span. With forecasting, we can recognize patterns and predict future incidents based on historical trends. This type of data analytics projects can be conducted using several models, including ARIMA (autoregressive integrated moving average), regression algorithms, and long short-term memory (LSTM).

• Collect the historical price data of the Dogecoin cryptocurrency
• Manipulate and clean the data
• Explore the data to have a better understanding
• Train a deep learning model to predict the future change in prices
• Deploy the model on a live server to predict the changes in real time

Deployed model into production integrated into a cryptocurrency trading web or mobile application. You can also build a dashboard based on the data insights to help understand the dynamics of Dogecoin.

• Dogecoin Historical Price Data

Project Overview

Flawed products can result in substantial financial losses, so defect detection is crucial in manufacturing. Although human detection systems are still the traditional method employed, computer vision techniques are more effective.

In this example, we build a system to detect defects in metallic objects or surfaces during different phases of the production processes.

The types of defects can be aesthetic, such as stains, or potentially damaging the product’s functionality, such as notches, scratches, burns, lack of rectification, bumps, burrs, flatness, lack of thread, countersunk, rust, or cracks.

Since the appearance of metallic surfaces changes substantially with different lighting, defects are hard to detect even using computer vision. For this reason, lighting is a crucial component in solving such types of data science problems. Otherwise, the methodology of this project is standard.

• Collect the metal surface defects dataset
• Data cleaning and exploration
• Feature extraction
• Train models for defects detection and classification
• Deploy the model into production on an embedded system

A deployed model on an embedded system that can detect and classify metallic surface defects in different conditions and environments.

• Metal Surface Defects Dataset
• Online Metallic Surface Defect Detection Using Deep Learning

Data Science Project Ideas: Next Steps

Having diverse and complex data science projects in your portfolio is a great way to demonstrate your skills to future employers. You can choose one from the list above or use it as inspiration and come up with your own idea.

But first, make sure you have the necessary skills to solve these problems. If you want to start with something simpler, try the 365 Data Science Career Track . That way, you can build your foundational knowledge and gradually progress to more advanced topics. In the meantime, the instructors will guide you through the completion of real-life data science projects. Sign up and start your learning journey with a selection of free courses.

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Youssef Hosni

Computer Vision Researcher / Data Scientist

Youssef is a computer vision researcher working towards his Ph.D. His research focuses on developing real-time computer vision algorithms for healthcare applications. He also worked as a data scientist, using customers' data to gain a better understanding of their behavior. Youssef is passionate about data and believes in AI's power to improve people's lives. He hopes to transfer his passion to others and guide them into this wide field through his writings.

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Capstone MB_Draft

Where Theory and Application Meet

What is a Capstone Project?

The Capstone Project is the pinnacle of the MDS program. It’s comprehensive in scope, allowing students to demonstrate the breadth and depth of knowledge in data science to an industry partner.  Students are given a tangible obstacle an organization is facing and are  tasked with extracting valuable insights, developing  an empirically-driven solution to sponsoring organizations that utilizes the full spectrum of the analytic process–data gathering, manipulation, visualization, analysis, and interpretation of the results.

Why should corporations, government and non-profit participate?

• Access to highly skilled data scientists that can propel them forward by injecting new ideas, fresh perspectives and innovative solutions to a strategic or operational problem.
• Elevate your organization’s brand and profile.
• Identify potential new hires. 
• Develop strong relationships with rising data scientists.

Interested in learning more? Hear from our past corporate sponsors and students here.  

Getting started

• Complete the one-page scoping/ planning form .
• Read and review the Sponsor Guide for best practices before the next step.
• Schedule a 30-minute qualification meeting.
• Past capstone projects examples: 2022,   2021

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Best 52 Data Science Project Ideas For Final Year

Are you interested in diving into the world of data science and machine learning? Well, you’re in the right place! Data science is a fascinating field that combines mathematics, statistics, and programming to extract meaningful insights from data. To get started on your data science journey, you’ll need some project ideas to practice your skills. In this blog, we’ll present 52 data science project ideas, with explanations for the first 10, to help you get started on your data-driven adventure.

What is Data Science?

Table of Contents

Data science is like a detective for data. It’s a way of using math, statistics, and computers to find valuable information hidden in big piles of data. Think of it as sorting through a jigsaw puzzle without knowing what the final picture looks like. Data scientists collect, clean, and analyze data to discover patterns, make predictions, and solve problems. They help businesses make smart decisions, like suggesting products you might like or finding ways to reduce costs. Data science is all about turning data into knowledge that can guide important choices in the world of business, science, and beyond.

10 Data Science Project Ideas For Final Year

1. predictive sales analysis.

Build a model that predicts future sales based on historical data. This project can help businesses optimize inventory and staffing.

2. Sentiment Analysis on Social Media Posts

Analyze Twitter or Reddit data to determine public sentiment about a specific topic, brand, or event.

3. Movie Recommendation System

Build a system that gives movie suggestions to users by looking at what they like and what they’ve watched before.

4. Credit Card Fraud Detection

Develop a model to identify fraudulent credit card transactions, helping banks and customers prevent financial loss.

5. Natural Language Processing (NLP) Chatbot

Build a chatbot that can engage in conversations, answer questions, and perform simple tasks using NLP techniques.

6. Image Classification

Train a model to classify images into predefined categories, like cats vs. dogs or handwritten digits recognition.

7. Housing Price Prediction

Make a tool that guesses how much a house costs in one place by looking at things like how big it is, how many bedrooms it has, and what neighborhood it’s in.

8. Customer Churn Analysis

Analyze customer behavior data to predict and reduce customer churn for businesses like subscription services.

9. Text Summarization

Create a text summarization tool that can automatically generate concise summaries of long articles or documents.

10. Anomaly Detection

Detect anomalies in time-series data, such as network traffic or equipment sensor readings, to identify unusual patterns or issues.

42 Data Science Project Ideas For Final Year

Now that you have a solid understanding of the first 10 data science project ideas, here are the names of the remaining 42 projects:

• Social Network Analysis
• Stock Price Prediction
• Email Spam Detection
• Language Translation Tool
• Customer Segmentation
• Weather Forecasting
• Healthcare Analytics
• Music Genre Classification
• E-commerce Product Recommendation
• Predictive Maintenance for Machinery
• Personality Prediction from Text
• Restaurant Reviews Sentiment Analysis
• Fraud Detection in Insurance Claims
• Image Style Transfer
• Predicting Disease Outbreaks
• Earnings Call Analysis
• Sports Analytics
• Traffic Congestion Prediction
• Employee Attrition Prediction
• Game Recommendation System
• News Topic Modeling
• Customer Lifetime Value Prediction
• Autonomous Drone Navigation
• Food Recipe Generator
• Movie Script Generation
• Fashion Style Recognition
• Energy Consumption Forecasting
• Environmental Pollution Monitoring
• Object Detection in Images
• Customer Support Chatbot
• Predictive Healthcare Diagnostics
• Vehicle License Plate Recognition
• Social Media Influence Analysis
• Image Super-Resolution
• Cybersecurity Threat Detection
• Demand Forecasting for Retail
• Stock Market Sentiment Analysis
• Music Lyrics Generation
• Voice Assistant for Data Analysis
• Political Opinion Mining
• Wildlife Species Identification
• Education Recommender System

Data science is an exciting field with endless possibilities. We’ve shared 52 data science project ideas to help you embark on your data science journey. The first 10 projects, from sales predictions to anomaly detection, offer a solid foundation to hone your skills.

As you explore these projects, remember that learning by doing is key. Start with projects that match your current skill level and gradually tackle more complex ones. Whether you’re interested in finance, healthcare, entertainment, or any other domain, there’s a data science project waiting for you.

By working on these projects, you’ll gain hands-on experience, build a portfolio, and develop the problem-solving skills crucial for a successful data science career. So, pick a project, gather your data, and start analyzing! With dedication and practice, you’ll be well on your way to becoming a proficient data scientist and making a meaningful impact with your data-driven insights.

Frequently Asked Questions

How can i start working on a data science project as a beginner .

Start with simple projects and learn from online tutorials. Python is a good language to begin with.

What’s the importance of data science in today’s world? 

Data science helps make informed decisions in various fields, from business to healthcare, by uncovering insights hidden in data.

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Analysis of data collected from 2021 Pew Research Center’s American Trends Panel

dfoster82/sql-for-data-science-capstone-project

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Sql for data science capstone project, 2021 pew research center’s american trends panel:, summary of survey findings.

This was my final capstone project for the Coursera Learn SQL Basics for Data Science Specialization. The final course consisted of four milestones:

Milestone 1: Project Proposal and Data Selection/Preparation

Milestone 2: Descriptive Stats and Understanding Your Data

Milestone 3: Beyond Descriptive Stats (Dive Deeper/Go Broader)

Milestone 4: Presenting Your Findings (Storytelling)

The numbered documents in this repository correspond to these milestones. I analyzed the data in a Jupyter notebook using Python, pandas, matplotlib, and Seaborn. The PowerPoint presentation for Milestone 4 summarizes the survey findings and includes data visualizations.

To view the complete notebook with all data visualizations, please view the notebook on Kaggle: https://www.kaggle.com/code/denisefoster/sql-for-data-science-capstone-project/notebook

• Jupyter Notebook 100.0%

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The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.

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The School of Information offers four degrees:

The Master of Information Management and Systems (MIMS) program educates information professionals to provide leadership for an information-driven world.

The Master of Information and Data Science (MIDS) is an online degree preparing data science professionals to solve real-world problems. The 5th Year MIDS program is a streamlined path to a MIDS degree for Cal undergraduates.

The Master of Information and Cybersecurity (MICS) is an online degree preparing cybersecurity leaders for complex cybersecurity challenges.

Our Ph.D. in Information Science is a research program for next-generation scholars of the information age.

• Fall 2024 Course Schedule

The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members.  More information about signing up for classes.

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Research by faculty members and doctoral students keeps the I School on the vanguard of contemporary information needs and solutions.

The I School is also home to several active centers and labs, including the Center for Long-Term Cybersecurity (CLTC) , the Center for Technology, Society & Policy , and the BioSENSE Lab .

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Data Science Spring 2021 Capstone Project Showcase

Capstone projects are the culmination of the MIDS students’ work in the School of Information’s Master of Information and Data Science program.

Over the course of their final semester, teams of students propose and select project ideas, conduct and communicate their work, receive and provide feedback, and deliver compelling presentations along with a web-based final deliverable.

Join us for an online presentation of these capstone projects. Each team will present for twenty minutes, including Q&A.

A panel of judges will select an outstanding project for the Hal R. Varian MIDS Capstone Award .

Log in for event video .

Sahab Aslam has unique and diverse experience ranging across data science, B2C life insurance products, digital health, product development, software engineering, and human-centric design in start-ups and Fortune 100 companies. Sahab started her digital health journey nine years ago, providing digital health solutions via SMS and voice recording technologies in underserved populations. Today, she utilizes data science to develop solutions and products to improve patients' lives and drive business impact at Merck. Sahab recently joined the MIDS faculty to share her passion for data science with her students. Sahab is an avid advocate of women in data science and technology. She mentors women who are early in their careers in the tech field. Sahab is an angel investor at Berkeley Alumni Network and advises many start-ups on product and data science. In her free time, she watches Star Wars spin-offs with her husband and two children. Sahab received her Master of Information & Data Science from the University of California, Berkeley. She holds an MS in mathematics and a bachelor’s in liberal arts and sciences.

Derek S. Chan  is a MIDS alumnus from Spring 2017, when he received the Hal R. Varian Award, followed by an informal Google partnership, and is AI product director at  Bill.com . His AI industry teams and products have helped companies reach their highest-ever customer satisfaction and/or revenue growth, plus #1 AP Automation 2020 and the IT World Award 2018. He enjoyed presenting topics and interacting with the MIDS community at Virtual Forum 2020 and DataEDGE 2018, and wishes you a fun and meaningful showcase!

Daniel Gillick is a research scientist at Google where his work focuses on natural language processing and machine intelligence. Dan is also an adjunct assistant professor at the UC Berkeley School of Information. He is course lead and developer for both applied machine learning and natural language processing with deep learning courses in the I School’s data science program.

Dr. Ramesh Sarukkai is a technologist in the valley where he has served in various leadership roles at Facebook, Google/YouTube, Lyft, Braintree/PayPa, working on delivering products and platforms used by billions of users and millions of businesses. He has a Ph.D. in computer science from the University of Rochester,  authored a book on Foundations of Web Technology (Springer/Kluwer), and holds over fifty patents. He is also active participant in panels/workshops in conferences on machine learning (e.g. NIPS self-driving workshop, CVPR).

More information

Spring 2021 MIDS Project Descriptions

If you have questions about this event, please contact the Student Affairs team at [email protected] .

Biomedical Graduate Education

Capstone Projects

2022-2023 graduates, nelson moore.

Data Scientist at Essential Software Inc

Capstone Project: Modeling and code implementation to support data search and filter through the NCI Cancer Data Aggregator Industry Mentor: Frederick National Lab for Cancer Research: FNLCR

Joelle Fitzgerald

Business Analyst at Ascension Health Care

Capstone Project: Analysis of patient safety event reports data. Industry Mentor: MedStar Health. National Center for Human Factors in Healthcare

Kader (Abdelkader) Bouregag

Healthcare Xplorer | Medical Informatics at Genentech (internship)

Capstone Project: Transforming the Immuno-Oncology data to the OMOP CDM Industry Mentor: MSKCC/ MedStar/ Georgetown University/ Hackensack

Junaid Imam

Data Scientist at Medstar Institute

Capstone Project: Create an [trans-] eQTL visualization tool

Industry Mentor: Pfizer Inc / Harvard

Abbie Gillen

Staff Data Analyst at Nice Healthcare

Capstone Project: Nice Healthcare: Predicting Nice healthcare utilization

Industry Mentor: Nice Healthcare

Capstone Project: Next Generation Data Commons

Industry Mentor: ICF International

2021-2022 Graduates

Ahson saiyed.

NLP Engineer/Data Scientist at TrinetX

Capstone Project : Research Data Platform Pipelines Industry Mentor: Invitae

Walid Nashashibi

Data Scientist at FEMA

Capstone Project: Xenopus RNA-Seq Analysis to Understand Tissue Regeneration Mechanisms Industry Mentor: FDA

Tony Albini

Data Analyst at ClearView Healthcare Partners

Capstone project: Data Mining to understand the patient landscape of Chronic Kidney Disease Population Industry Mentor: AstraZeneca

Anvitha Gooty Agraharam

Business Account Manager at GeneData

Capstone Project: Computational estimation of Pleiotropy in Genome-Phenome Associations for target discovery Industry Mentor: AstraZeneca

Natalie Cortopassi

Researcher at the Institute for Health Metrics and Evaluation

Capstone project: Analysis of Clinical Trial Attrition in Neuropsychiatric Clinical Trials using Machine Learning Industry Mentor: AstraZeneca

Christle Iroezi

Business System Analyst at Centene Corporation

Capstone project: Visualize Digital HealthCare ROI Industry Mentor: MedStar Health

R & D Analyst II at GEICO

Capstone project: Heat Waves and Health Outcomes Industry Mentor: ICF

Research Specialist at Georgetown University

Capstone project: Mental Health Data Commons Industry Mentor: ICF

2020-2021 Graduates

Technology Transformation Analyst, Grant Thornton LLP

Capstone Project: Research Data Platform Pipelines Industry Mentor: Invitae

Research Technician at Georgetown University

Capstone Project: Using a configurable, open-source framework to create a fully functional data commons with the REMBRANDT dataset Industry Mentor: Frederick National Lab for Cancer Research – FNLCR

Consultant at Deloitte

Capstone Project: Building a patient centric data warehouse Industry Mentor: Invitae

Marcio Rosas

Project Manager of Technology and Informatics at Georgetown University

Capstone Project: Knowledge-Based Predictive Modeling of Clinical Trials Enrollment Rates Industry Mentor : AstraZeneca

Yuezheng (Kerry) He

Data Product Associate at YipitData

Capstone Project: ClinicalTrials2Vec – Accelerating trial-level computing using a vectorized model of clinical trial summaries and results Industry Mentor: AstraZeneca

Data Programmer at Chemonics International

Capstone Project: Multi-scale modeling to enable data-driven biomarker and target discovery Industry Mentor: AstraZeneca

2019-2020 Graduates

Pratyush tandale.

Informatics Specialist I at Mayo Clinic

Capstone Project: Improving clinical mapping process for lab data using LOINC Industry Mentor: Flatiron Roche

Shabeeb Kannattikuni

Senior Statistical Programmer at PRA Health Sciences (ICON Pl)

Capstone Project: NGS Data Analysis for the QA of viral vaccines Industry Mentor: Argentys Informatics

Fuyuan Wang (Bruce)

Software Engineer at Essential Software Inc , Frederick National Labs

Capstone Project: Cancer Data Model Visualization framework Industry Mentor: Frederick National Laboratory for Cancer Research

Ayah Elshikh

Capstone Project: NGS Data Analysis for the QA of viral vaccines

Industry Mentor: Argentys Informatics

Yue (Lilian) Li

Biostatistician and Statistical Programmer , Baim Institute for Clinical Research

Capstone Project: Analysis of COVID-19 Serological test data to improve the COVID-19 Detection capabalities Industry Mentor: Argentys Informatics

Algorithm Performance Engineer at Optovue

Capstone Project: Socioeconomic factors to readmissions after major cancer surgery Industry Mentor: Medstar Health

Jiazhong Zhang

Management Trainee at China Bohai Bank

Jianyi Zhang

Fall 2021 Capstone Presentations

Tuesday, December 14, 2021 9:00 am - 12:00 pm

The Capstone course provides a unique opportunity for students in the M.S. in Data Science program to apply their knowledge of the foundations, theory and methods of data science to address data driven problems in industry, government and the non-profit sector. Course activities focus on a semester-length project sponsored by a local organization. The resulting projects synthesize the statistical, computational, engineering and social challenges involved in solving complex real-world problems.

Join our event to explore the projects, see demos, and meet with the participating students and mentors. Find project themes and companies below. 

Event Date & Time

Tuesday, december 14   (2:00 pm – 5:00 pm et) — virtual.

2:00 PM: Join the Event.  The event will be held on  Gatherly , an interactive virtual platform where guests can walk around and meet new people, just like in real life. Attendees can navigate Gatherly floors, designed based on Capstone project topic area, where students will stand by their project to give short presentations and answer questions.

2:05 PM: Introduction from Capstone Faculty.  Learn more about the Capstone program and its impact across the Data Science Institute and Columbia University at large.

2:10 PM: Presentations.  Presentations will be open until 5:00 PM ET; guests are welcome to float in and out of Gatherly floors to see all of the demos, or focus on exploring projects within areas of interest.

• Floor 1: Natural Language Processing (NLP)
• Floor 2: Neural Networks & Time Series
• Floor 3 : Fairness & Machine Learning

5:00 PM:  Event ends.

Navigating the Event

Welcome floor.

Access the DSI help desk, where representatives of our student services team will be available to assist you. Move your mouse to the elevators, where you can head to the floors to see the student projects.

Floor 1 : Natural Language Processing (NLP)

POSTER 1: Using Natural Language Processing to Discover COVD-19 Impacts on Birthing Families from Social Media

• Mentors: Adam Poliak, Caitlin Dreisbach
• Students: Xiaoyan Li, Neha Santhoshi Pusarla, Miranda Gao Zhou, Lu Bin Liu, Gaoyi Shi

POSTER 2: The Power of Peace Speech

• Earth Institute | LD
• Mentor: Peter Coleman
• Students: Haoyue Qi, Yuxin Zhou, Xuanhao Wu, Hongling Liu, Wenjie Zhu

POSTER 3: Generating Related Work Sections for Scientific Papers: Part 1: Domains

• Mentor: Anita de Waard
• Students: Tingyi Lu, Yifan Jing, Jiayin Lin, Yuhe Wang

POSTER 4: Measuring Strategic Pivots

• Graduate School of Business
• Mentor: Jorge Guzman
• Students: Heather Zhu, Weiyao Xie, Ningxin Li, Angela Zhou, Sally Bao

POSTER 5: Automated Data Labeling with NLP and Active Learning

• Mentors: Steven Agajanian, Kurt Vile, Evan David, Ji Zhang
• Students: Chelsea Cui, Zhibin Li, Jingyan Xu, Yuan Cheng, Yifei Zhang

POSTER 6:   Polling, ObamaCare, Mainstream News Spreading Misinformation

• Microsoft Research NYC
• Mentors: David Rothschild
• Students: Kevin Gao

Floor 2 : Neural Networks & Time Series

POSTER 7: Neural Semantic Proto-Role Labeler

• Mentors: Yuval Marton, Asad Sayeed
• Students: Sai Thrinath Gunda, Tarun Devireddy, Mitali Bante, Sriram Dommeti

POSTER 8: Graph Machine Learning for Mixed Data Sources

• Mentors: Naftali Cohen, Srijan Sood, Zhen Zeng
• Students: Bo Hu, Qin Rui, Wenxuan Liu, Erdong Wang, Shuibenyang Yuan

POSTER 9: Assistive Robot: Recognize and Engage with People

• Students: Wenjun Cheng, Kenny Jin, Jiongxin Ye, Xiaoyu Su, Yuzheng Jia

POSTER 10: Hierarchical Time Series Forecasting

• Students: Diyue Gu, Zujun Peng, Yifei Chen, Haichao Yi, Yilan Jiang

POSTER 11: Machine Learning Model for Atomic Structure of Sustainable Energy Materials

• Mentor: Simon Billinge
• Students: Qiran Li, Jingyuan Li, Chaoying Zheng, James Ding, Sidney Fletcher

POSTER 12: Overall Market Earnings Growth Forecasting

• Mentor: Nicholas Abell, Ryan Deming, Sydney Son
• Students: Chenxi Di, Nan Tang, Liyuan Tang, Tianqi Lou, Yuxin Qian

POSTER 13: Impact Estimation of New Competitors in Markets with Simultaneous Events

• Mentors: Gerard Sanz-Estape, Laura Rodriguez-Gomez, Javier Cerezo
• Students: Wendy Qian, Hanlin Tong, Lihui Pan, Zhiheng Jiang, Yishi Wang

POSTER 14: Intraday Volatility

• Mentor: Lada Kyj
• Students: Sung-Kuk Lim, Young Hoo Cho, Sanket Sunil Gokhale, Minwoo Choi, Chenchao You

Floor 3: Fairness & Machine Learning

POSTER 15: Climate Justice: Quantifying the Impacts of Floods on Socially-Vulnerable People in the US

• Mentor: Marco Tedesco
• Students: Tomislav Galjanic, Christodoulos Constantinides, Abhishek Sinha, Samir Char

POSTER 16: Predicting Pharmaceutical Usage and Adverse Effects

• Goldman Sachs
• Mentor: Joe Kogan
• Students: Aditya Koduri, Archit Matta, Karunakar Gadireddy, Shivani Modi, Yosha Singh Tomar

POSTER 17: Predictive Models to Understand Patient Risks in Orthopedics

• Johnson & Johnson
• Mentor: Chin-Wen Chang
• Students: Guotian Zhu, Pan Jiayi, Cai Yiwen, Yidan Gao, Lingxuan Gu

POSTER 18: Algorithmic Fairness in Healthcare

• Mentor: Thibaut Galvain, Cindy Tong
• Students: Jingyi An, Jialu Xia, Yuanhang Chen, Dingwen Xie, Run Zhang

POSTER 19: Market Basket Analysis

• Ralph Lauren
• Mentor: Nandakumar Sudha
• Students: Keertan Krishnan, Rahul Agarwal, Rahul Subramaniam, Shaurya Malik, Myles Ingram

POSTER 20: Causality-Informed Fairness Treatments of Unfair AI Systems

• Mentor: Sanghamitra Dutta, Naftali Cohen
• Students: Oscar Jasklowski, Yue Wang, Mohammed Aqid Khatkhatay, Xue Gu, Junzhi Ge

Capstone Faculty

Sining Chen , Adjunct Professor of Industrial Engineering and Operations Research, Columbia University

Adam S. Kelleher , Adjunct Assistant Professor of Computer Science, Columbia University

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25+ Solved End-to-End Big Data Projects with Source Code

Solved End-to-End Real World Mini Big Data Projects Ideas with Source Code For Beginners and Students to master big data tools like Hadoop and Spark.

Ace your big data analytics interview by adding some unique and exciting Big Data projects to your portfolio. This blog lists over 20 big data analytics projects you can work on to showcase your big data skills and gain hands-on experience in big data tools and technologies. You will find several big data projects depending on your level of expertise- big data projects for students, big data projects for beginners, etc.

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Have you ever looked for sneakers on Amazon and seen advertisements for similar sneakers while searching the internet for the perfect cake recipe? Maybe you started using Instagram to search for some fitness videos, and now, Instagram keeps recommending videos from fitness influencers to you. And even if you’re not very active on social media, I’m sure you now and then check your phone before leaving the house to see what the traffic is like on your route to know how long it could take you to reach your destination. None of this would have been possible without the application of big data analysis process on by the modern data driven companies. We bring the top big data projects for 2023 that are specially curated for students, beginners, and anybody looking to get started with mastering data skills.

Table of Contents

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A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on structured and unstructured data for several purposes, including predictive modeling and other advanced analytics applications. Before actually working on any big data projects, data engineers must acquire proficient knowledge in the relevant areas, such as deep learning, machine learning, data visualization , data analytics, data science, etc. 

Many platforms, like GitHub and ProjectPro, offer various big data projects for professionals at all skill levels- beginner, intermediate, and advanced. However, before moving on to a list of big data project ideas worth exploring and adding to your portfolio, let us first get a clear picture of what big data is and why everyone is interested in it.

Kicking off a big data analytics project is always the most challenging part. You always encounter questions like what are the project goals, how can you become familiar with the dataset, what challenges are you trying to address,  what are the necessary skills for this project, what metrics will you use to evaluate your model, etc.

Well! The first crucial step to launching your project initiative is to have a solid project plan. To build a big data project, you should always adhere to a clearly defined workflow. Before starting any big data project, it is essential to become familiar with the fundamental processes and steps involved, from gathering raw data to creating a machine learning model to its effective implementation.

Understand the Business Goals of the Big Data Project

The first step of any good big data analytics project is understanding the business or industry that you are working on. Go out and speak with the individuals whose processes you aim to transform with data before you even consider analyzing the data. Establish a timeline and specific key performance indicators afterward. Although planning and procedures can appear tedious, they are a crucial step to launching your data initiative! A definite purpose of what you want to do with data must be identified, such as a specific question to be answered, a data product to be built, etc., to provide motivation, direction, and purpose.

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Collect Data for the Big Data Project

The next step in a big data project is looking for data once you've established your goal. To create a successful data project, collect and integrate data from as many different sources as possible. 

Here are some options for collecting data that you can utilize:

Connect to an existing database that is already public or access your private database.

Consider the APIs for all the tools your organization has been utilizing and the data they have gathered. You must put in some effort to set up those APIs so that you can use the email open and click statistics, the support request someone sent, etc.

There are plenty of datasets on the Internet that can provide more information than what you already have. There are open data platforms in several regions (like data.gov in the U.S.). These open data sets are a fantastic resource if you're working on a personal project for fun.

Data Preparation and Cleaning

The data preparation step, which may consume up to 80% of the time allocated to any big data or data engineering project, comes next. Once you have the data, it's time to start using it. Start exploring what you have and how you can combine everything to meet the primary goal. To understand the relevance of all your data, start making notes on your initial analyses and ask significant questions to businesspeople, the IT team, or other groups. Data Cleaning is the next step. To ensure that data is consistent and accurate, you must review each column and check for errors, missing data values, etc.

Making sure that your project and your data are compatible with data privacy standards is a key aspect of data preparation that should not be overlooked. Personal data privacy and protection are becoming increasingly crucial, and you should prioritize them immediately as you embark on your big data journey. You must consolidate all your data initiatives, sources, and datasets into one location or platform to facilitate governance and carry out privacy-compliant projects. 

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Data Transformation and Manipulation

Now that the data is clean, it's time to modify it so you can extract useful information. Starting with combining all of your various sources and group logs will help you focus your data on the most significant aspects. You can do this, for instance, by adding time-based attributes to your data, like:

Acquiring date-related elements (month, hour, day of the week, week of the year, etc.)

Calculating the variations between date-column values, etc.

Joining datasets is another way to improve data, which entails extracting columns from one dataset or tab and adding them to a reference dataset. This is a crucial component of any analysis, but it can become a challenge when you have many data sources.

 Visualize Your Data

Now that you have a decent dataset (or perhaps several), it would be wise to begin analyzing it by creating beautiful dashboards, charts, or graphs. The next stage of any data analytics project should focus on visualization because it is the most excellent approach to analyzing and showcasing insights when working with massive amounts of data.

Another method for enhancing your dataset and creating more intriguing features is to use graphs. For instance, by plotting your data points on a map, you can discover that some geographic regions are more informative than some other nations or cities.

Build Predictive Models Using Machine Learning Algorithms

Machine learning algorithms can help you take your big data project to the next level by providing you with more details and making predictions about future trends. You can create models to find trends in the data that were not visible in graphs by working with clustering techniques (also known as unsupervised learning). These organize relevant outcomes into clusters and more or less explicitly state the characteristic that determines these outcomes.

Advanced data scientists can use supervised algorithms to predict future trends. They discover features that have influenced previous data patterns by reviewing historical data and can then generate predictions using these features. 

Lastly, your predictive model needs to be operationalized for the project to be truly valuable. Deploying a machine learning model for adoption by all individuals within an organization is referred to as operationalization.

Repeat The Process

This is the last step in completing your big data project, and it's crucial to the whole data life cycle. One of the biggest mistakes individuals make when it comes to machine learning is assuming that once a model is created and implemented, it will always function normally. On the contrary, if models aren't updated with the latest data and regularly modified, their quality will deteriorate with time.

You need to accept that your model will never indeed be "complete" to accomplish your first data project effectively. You need to continually reevaluate, retrain it, and create new features for it to stay accurate and valuable. 

If you are a newbie to Big Data, keep in mind that it is not an easy field, but at the same time, remember that nothing good in life comes easy; you have to work for it. The most helpful way of learning a skill is with some hands-on experience. Below is a list of Big Data analytics project ideas and an idea of the approach you could take to develop them; hoping that this could help you learn more about Big Data and even kick-start a career in Big Data. 

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In this section, you will find a list of good big data project ideas for masters students.

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Project Ideas on Big Data Analytics

Let us now begin with a more detailed list of good big data project ideas that you can easily implement.

This section will introduce you to a list of project ideas on big data that use Hadoop along with descriptions of how to implement them.

1. Visualizing Wikipedia Trends

Human brains tend to process visual data better than data in any other format. 90% of the information transmitted to the brain is visual, and the human brain can process an image in just 13 milliseconds. Wikipedia is a page that is accessed by people all around the world for research purposes, general information, and just to satisfy their occasional curiosity. 

Raw page data counts from Wikipedia can be collected and processed via Hadoop. The processed data can then be visualized using Zeppelin notebooks to analyze trends that can be supported based on demographics or parameters. This is a good pick for someone looking to understand how big data analysis and visualization can be achieved through Big Data and also an excellent pick for an Apache Big Data project idea. 

Visualizing Wikipedia Trends Big Data Project with Source Code .

2. Visualizing Website Clickstream Data

Clickstream data analysis refers to collecting, processing, and understanding all the web pages a particular user visits. This analysis benefits web page marketing, product management, and targeted advertisement. Since users tend to visit sites based on their requirements and interests, clickstream analysis can help to get an idea of what a user is looking for. 

Visualization of the same helps in identifying these trends. In such a manner, advertisements can be generated specific to individuals. Ads on webpages provide a source of income for the webpage, and help the business publishing the ad reach the customer and at the same time, other internet users. This can be classified as a Big Data Apache project by using Hadoop to build it.

Big Data Analytics Projects Solution for Visualization of Clickstream Data on a Website

3. Web Server Log Processing

A web server log maintains a list of page requests and activities it has performed. Storing, processing, and mining the data on web servers can be done to analyze the data further. In this manner, webpage ads can be determined, and SEO (Search engine optimization) can also be done. A general overall user experience can be achieved through web-server log analysis. This kind of processing benefits any business that heavily relies on its website for revenue generation or to reach out to its customers. The Apache Hadoop open source big data project ecosystem with tools such as Pig, Impala, Hive, Spark, Kafka Oozie, and HDFS can be used for storage and processing.

Big Data Project using Hadoop with Source Code for Web Server Log Processing 

This section will provide you with a list of projects that utilize Apache Spark for their implementation.

4. Analysis of Twitter Sentiments Using Spark Streaming

Sentimental analysis is another interesting big data project topic that deals with the process of determining whether a given opinion is positive, negative, or neutral. For a business, knowing the sentiments or the reaction of a group of people to a new product launch or a new event can help determine the profitability of the product and can help the business to have a more extensive reach by getting an idea of the feel of the customers. From a political standpoint, the sentiments of the crowd toward a candidate or some decision taken by a party can help determine what keeps a specific group of people happy and satisfied. You can use Twitter sentiments to predict election results as well. 

Sentiment analysis has to be done for a large dataset since there are over 180 million monetizable daily active users ( https://www.businessofapps.com/data/twitter-statistics/) on Twitter. The analysis also has to be done in real-time. Spark Streaming can be used to gather data from Twitter in real time. NLP (Natural Language Processing) models will have to be used for sentimental analysis, and the models will have to be trained with some prior datasets. Sentiment analysis is one of the more advanced projects that showcase the use of Big Data due to its involvement in NLP.

Access Big Data Project Solution to Twitter Sentiment Analysis

5. Real-time Analysis of Log-entries from Applications Using Streaming Architectures

If you are looking to practice and get your hands dirty with a real-time big data project, then this big data project title must be on your list. Where web server log processing would require data to be processed in batches, applications that stream data will have log files that would have to be processed in real-time for better analysis. Real-time streaming behavior analysis gives more insight into customer behavior and can help find more content to keep the users engaged. Real-time analysis can also help to detect a security breach and take necessary action immediately. Many social media networks work using the concept of real-time analysis of the content streamed by users on their applications. Spark has a Streaming tool that can process real-time streaming data.

Access Big Data Spark Project Solution to Real-time Analysis of log-entries from applications using Streaming Architecture

6. Analysis of Crime Datasets

Analysis of crimes such as shootings, robberies, and murders can result in finding trends that can be used to keep the police alert for the likelihood of crimes that can happen in a given area. These trends can help to come up with a more strategized and optimal planning approach to selecting police stations and stationing personnel. 

With access to CCTV surveillance in real-time, behavior detection can help identify suspicious activities. Similarly, facial recognition software can play a bigger role in identifying criminals. A basic analysis of a crime dataset is one of the ideal Big Data projects for students. However, it can be made more complex by adding in the prediction of crime and facial recognition in places where it is required.

Big Data Analytics Projects for Students on Chicago Crime Data Analysis with Source Code

Explore Categories

In this section, you will find big data projects that rely on cloud service providers such as AWS and GCP.

7. Build a Scalable Event-Based GCP Data Pipeline using DataFlow

Suppose you are running an eCommerce website, and a customer places an order. In that case, you must inform the warehouse team to check the stock availability and commit to fulfilling the order. After that, the parcel has to be assigned to a delivery firm so it can be shipped to the customer. For such scenarios, data-driven integration becomes less comfortable, so you must prefer event-based data integration.

This project will teach you how to design and implement an event-based data integration pipeline on the Google Cloud Platform by processing data using DataFlow .

Data Description: You will use the Covid-19 dataset(COVID-19 Cases.csv) from data.world , for this project, which contains a few of the following attributes:

people_positive_cases_count

county_name

data_source

Language Used: Python 3.7

Services: Cloud Composer , Google Cloud Storage (GCS), Pub-Sub , Cloud Functions, BigQuery, BigTable

Big Data Project with Source Code: Build a Scalable Event-Based GCP Data Pipeline using DataFlow  

8. Topic Modeling

The future is AI! You must have come across similar quotes about artificial intelligence (AI). Initially, most people found it difficult to believe that could be true. Still, we are witnessing top multinational companies drift towards automating tasks using machine learning tools. 

Understand the reason behind this drift by working on one of our repository's most practical data engineering project examples .

Project Objective: Understand the end-to-end implementation of Machine learning operations (MLOps) by using cloud computing .

Learnings from the Project: This project will introduce you to various applications of AWS services . You will learn how to convert an ML application to a Flask Application and its deployment using Gunicord webserver. You will be implementing this project solution in Code Build. This project will help you understand ECS Cluster Task Definition.

Tech Stack:

Language: Python

Libraries: Flask, gunicorn, scipy , nltk , tqdm, numpy, joblib, pandas, scikit_learn, boto3

Services: Flask, Docker, AWS, Gunicorn

Source Code: MLOps AWS Project on Topic Modeling using Gunicorn Flask

9. MLOps on GCP Project for Autoregression using uWSGI Flask

Here is a project that combines Machine Learning Operations (MLOps) and Google Cloud Platform (GCP). As companies are switching to automation using machine learning algorithms, they have realized hardware plays a crucial role. Thus, many cloud service providers have come up to help such companies overcome their hardware limitations. Therefore, we have added this project to our repository to assist you with the end-to-end deployment of a machine learning project .

Project Objective: Deploying the moving average time-series machine-learning model on the cloud using GCP and Flask.

Learnings from the Project: You will work with Flask and uWSGI model files in this project. You will learn about creating Docker Images and Kubernetes architecture. You will also get to explore different components of GCP and their significance. You will understand how to clone the git repository with the source repository. Flask and Kubernetes deployment will also be discussed in this project.

Tech Stack: Language - Python

Services - GCP, uWSGI, Flask, Kubernetes, Docker

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This section has good big data project ideas for graduate students who have enrolled in a master course.

10. Real-time Traffic Analysis

Traffic is an issue in many major cities, especially during some busier hours of the day. If traffic is monitored in real-time over popular and alternate routes, steps could be taken to reduce congestion on some roads. Real-time traffic analysis can also program traffic lights at junctions – stay green for a longer time on higher movement roads and less time for roads showing less vehicular movement at a given time. Real-time traffic analysis can help businesses manage their logistics and plan their commute accordingly for working-class individuals. Concepts of deep learning can be used to analyze this dataset properly.

11. Health Status Prediction

“Health is wealth” is a prevalent saying. And rightly so, there cannot be wealth unless one is healthy enough to enjoy worldly pleasures. Many diseases have risk factors that can be genetic, environmental, dietary, and more common for a specific age group or sex and more commonly seen in some races or areas. By gathering datasets of this information relevant for particular diseases, e.g., breast cancer, Parkinson’s disease, and diabetes, the presence of more risk factors can be used to measure the probability of the onset of one of these issues. 

In cases where the risk factors are not already known, analysis of the datasets can be used to identify patterns of risk factors and hence predict the likelihood of onset accordingly. The level of complexity could vary depending on the type of analysis that has to be done for different diseases. Nevertheless, since prediction tools have to be applied, this is not a beginner-level big data project idea.

12. Analysis of Tourist Behavior

Tourism is a large sector that provides a livelihood for several people and can adversely impact a country's economy.. Not all tourists behave similarly simply because individuals have different preferences. Analyzing this behavior based on decision-making, perception, choice of destination, and level of satisfaction can be used to help travelers and locals have a more wholesome experience. Behavior analysis, like sentiment analysis, is one of the more advanced project ideas in the Big Data field.

13. Detection of Fake News on Social Media

With the popularity of social media, a major concern is the spread of fake news on various sites. Even worse, this misinformation tends to spread even faster than factual information. According to Wikipedia, fake news can be visual-based, which refers to images, videos, and even graphical representations of data, or linguistics-based, which refers to fake news in the form of text or a string of characters. Different cues are used based on the type of news to differentiate fake news from real. A site like Twitter has 330 million users , while Facebook has 2.8 billion users. A large amount of data will make rounds on these sites, which must be processed to determine the post's validity. Various data models based on machine learning techniques and computational methods based on NLP will have to be used to build an algorithm that can be used to detect fake news on social media.

Access Solution to Interesting Big Data Project on Detection of Fake News

14. Prediction of Calamities in a Given Area

Certain calamities, such as landslides and wildfires, occur more frequently during a particular season and in certain areas. Using certain geospatial technologies such as remote sensing and GIS (Geographic Information System) models makes it possible to monitor areas prone to these calamities and identify triggers that lead to such issues. 

If calamities can be predicted more accurately, steps can be taken to protect the residents from them, contain the disasters, and maybe even prevent them in the first place. Past data of landslides has to be analyzed, while at the same time, in-site ground monitoring of data has to be done using remote sensing. The sooner the calamity can be identified, the easier it is to contain the harm. The need for knowledge and application of GIS adds to the complexity of this Big Data project.

15. Generating Image Captions

With the emergence of social media and the importance of digital marketing, it has become essential for businesses to upload engaging content. Catchy images are a requirement, but captions for images have to be added to describe them. The additional use of hashtags and attention-drawing captions can help a little more to reach the correct target audience. Large datasets have to be handled which correlate images and captions. 

This involves image processing and deep learning to understand the image and artificial intelligence to generate relevant but appealing captions. Python can be used as the Big Data source code. Image caption generation cannot exactly be considered a beginner-level Big Data project idea. It is probably better to get some exposure to one of the projects before proceeding with this.

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16. Credit Card Fraud Detection

The goal is to identify fraudulent credit card transactions, so a customer is not billed for an item that the customer did not purchase. This can tend to be challenging since there are huge datasets, and detection has to be done as soon as possible so that the fraudsters do not continue to purchase more items. Another challenge here is the data availability since the data is supposed to be primarily private. Since this project involves machine learning, the results will be more accurate with a larger dataset. Data availability can pose a challenge in this manner. Credit card fraud detection is helpful for a business since customers are likely to trust companies with better fraud detection applications, as they will not be billed for purchases made by someone else. Fraud detection can be considered one of the most common Big Data project ideas for beginners and students.

If you are looking for big data project examples that are fun to implement then do not miss out on this section.

17. GIS Analytics for Better Waste Management

Due to urbanization and population growth, large amounts of waste are being generated globally. Improper waste management is a hazard not only to the environment but also to us. Waste management involves the process of handling, transporting, storing, collecting, recycling, and disposing of the waste generated. Optimal routing of solid waste collection trucks can be done using GIS modeling to ensure that waste is picked up, transferred to a transfer site, and reaches the landfills or recycling plants most efficiently. GIS modeling can also be used to select the best sites for landfills. The location and placement of garbage bins within city localities must also be analyzed. 

18. Customized Programs for Students

We all tend to have different strengths and paces of learning. There are different kinds of intelligence, and the curriculum only focuses on a few things. Data analytics can help modify academic programs to nurture students better. Programs can be designed based on a student’s attention span and can be modified according to an individual’s pace, which can be different for different subjects. E.g., one student may find it easier to grasp language subjects but struggle with mathematical concepts.

In contrast, another might find it easier to work with math but not be able to breeze through language subjects. Customized programs can boost students’ morale, which could also reduce the number of dropouts. Analysis of a student’s strong subjects, monitoring their attention span, and their responses to specific topics in a subject can help build the dataset to create these customized programs.

19. Real-time Tracking of Vehicles

Transportation plays a significant role in many activities. Every day, goods have to be shipped across cities and countries; kids commute to school, and employees have to get to work. Some of these modes might have to be closely monitored for safety and tracking purposes. I’m sure parents would love to know if their children’s school buses were delayed while coming back from school for some reason. 

Taxi applications have to keep track of their users to ensure the safety of the drivers and the users. Tracking has to be done in real-time, as the vehicles will be continuously on the move. Hence, there will be a continuous stream of data flowing in. This data has to be processed, so there is data available on how the vehicles move so that improvements in routes can be made if required but also just for information on the general whereabouts of the vehicle movement.

20. Analysis of Network Traffic and Call Data Records

There are large chunks of data-making rounds in the telecommunications industry. However, very little of this data is currently being used to improve the business. According to a MindCommerce study: “An average telecom operator generates billions of records per day, and data should be analyzed in real or near real-time to gain maximum benefit.” 

The main challenge here is that these large amounts of data must be processed in real-time. With big data analysis, telecom industries can make decisions that can improve the customer experience by monitoring the network traffic. Issues such as call drops and network interruptions must be closely monitored to be addressed accordingly. By evaluating the usage patterns of customers, better service plans can be designed to meet these required usage needs. The complexity and tools used could vary based on the usage requirements of this project.

This section contains project ideas in big data that are primarily open-source and have been developed by Apache.

Apache Hadoop is an open-source big data processing framework that allows distributed storage and processing of large datasets across clusters of commodity hardware. It provides a scalable, reliable, and cost-effective solution for processing and analyzing big data.

22. Apache Spark

Apache Spark is an open-source big data processing engine that provides high-speed data processing capabilities for large-scale data processing tasks. It offers a unified analytics platform for batch processing, real-time processing, machine learning, and graph processing.

23. Apache Nifi 

Apache NiFi is an open-source data integration tool that enables users to easily and securely transfer data between systems, databases, and applications. It provides a web-based user interface for creating, scheduling, and monitoring data flows, making it easy to manage and automate data integration tasks.

24. Apache Flink

Apache Flink is an open-source big data processing framework that provides scalable, high-throughput, and fault-tolerant data stream processing capabilities. It offers low-latency data processing and provides APIs for batch processing, stream processing, and graph processing.

25. Apache Storm

Apache Storm is an open-source distributed real-time processing system that provides scalable and fault-tolerant stream processing capabilities. It allows users to process large amounts of data in real-time and provides APIs for creating data pipelines and processing data streams.

Does Big Data sound difficult to work with? Work on end-to-end solved Big Data Projects using Spark , and you will know how easy it is!

This section has projects on big data along with links of their source code on GitHub.

26. Fruit Image Classification

This project aims to make a mobile application to enable users to take pictures of fruits and get details about them for fruit harvesting. The project develops a data processing chain in a big data environment using Amazon Web Services (AWS) cloud tools, including steps like dimensionality reduction and data preprocessing and implements a fruit image classification engine. 

The project involves generating PySpark scripts and utilizing the AWS cloud to benefit from a Big Data architecture (EC2, S3, IAM) built on an EC2 Linux server. This project also uses DataBricks since it is compatible with AWS.

Source Code: Fruit Image Classification

27. Airline Customer Service App

In this project, you will build a web application that uses machine learning and Azure data bricks to forecast travel delays using weather data and airline delay statistics. Planning a bulk data import operation is the first step in the project. Next comes preparation, which includes cleaning and preparing the data for testing and building your machine learning model. 

This project will teach you how to deploy the trained model to Docker containers for on-demand predictions after storing it in Azure Machine Learning Model Management. It transfers data using Azure Data Factory (ADF) and summarises data using Azure Databricks and Spark SQL . The project uses Power BI to visualize batch forecasts.

Source Code: Airline Customer Service App

28. Criminal Network Analysis

This fascinating big data project seeks to find patterns to predict and detect links in a dynamic criminal network. This project uses a stream processing technique to extract relevant information as soon as data is generated since the criminal network is a dynamic social graph. It also suggests three brand-new social network similarity metrics for criminal link discovery and prediction. The next step is to develop a flexible data stream processing application using the Apache Flink framework, which enables the deployment and evaluation of the newly proposed and existing metrics.

Source Code- Criminal Network Analysis

Trying out these big data project ideas mentioned above in this blog will help you get used to the popular tools in the industry. But these projects are not enough if you are planning to land a job in the big data industry. And if you are curious about what else will get you closer to landing your dream job, then we highly recommend you check out ProjectPro . ProjectPro hosts a repository of solved projects in Data Science and Big Data prepared by experts in the industry. It offers a subscription to that repository that contains solutions in the form of guided videos along with supporting documentation to help you understand the projects end-to-end. So, don’t wait more to get your hands dirty with ProjectPro projects and subscribe to the repository today!

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1. Why are big data projects important?

Big data projects are important as they will help you to master the necessary big data skills for any job role in the relevant field. These days, most businesses use big data to understand what their customers want, their best customers, and why individuals select specific items. This indicates a huge demand for big data experts in every industry, and you must add some good big data projects to your portfolio to stay ahead of your competitors.

2. What are some good big data projects?

Design a Network Crawler by Mining Github Social Profiles. In this big data project, you'll work on a Spark GraphX Algorithm and a Network Crawler to mine the people relationships around various Github projects.

Visualize Daily Wikipedia Trends using Hadoop - You'll build a Spark GraphX Algorithm and a Network Crawler to mine the people relationships around various Github projects. 

Modeling & Thinking in Graphs(Neo4J) using Movielens Dataset - You will reconstruct the movielens dataset in a graph structure and use that structure to answer queries in various ways in this Neo4j big data project.

3. How long does it take to complete a big data project?

A big data project might take a few hours to hundreds of days to complete. It depends on various factors such as the type of data you are using, its size, where it's stored, whether it is easily accessible, whether you need to perform any considerable amount of ETL processing on the data, etc. 

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• 14 10 Public Health Capstone Project Ideas
• 15 10 Political Science Capstone Project Ideas
• 16 10 Best Capstone Project Ideas in Economics
• 17 10 Sociology Capstone Project Ideas
• 18 Capstone Writing: 10 Essential Steps

The long path of research works ahead, and you can’t find any capstone project ideas that would be interesting and innovative. The task can seem even more challenging for you to feel all the responsibility of this first step. The top 200+ capstone ideas presented below aim to make a not-so-effort-consuming choice.

These ideas cover a wide range of academic subjects, making sure you find something that matches your interests and goals. Explore this list to find varied topics for capstone projects in areas like information technology, nursing, psychology, marketing, and management. Continue reading and feel inspired to start your capstone project with confidence. Remember, the right choice can greatly affect your academic and professional future.

What is a Capstone Project?

Educational institutions use the capstone project to evaluate your understanding of the course on various parameters. For the students, the work on the project gives an excellent opportunity to demonstrate their presentation, problem-solving and soft skills. Capstone projects are normally used in the curriculum of colleges and schools. Also called a senior exhibition or a culminating project, such assignments mark the end of a course.

This assignment has several different objectives, among which are the following:

• to encourage independent planning,
• to learn to meet up deadlines,
• to practice a detailed analysis,
• to work in teams.

It’s not that easy to pick the right capstone paper topic. The problem intensifies as each student or separate team have to work on a single assignment which has to be unique. The best capstone project ideas may possibly run out. However, whatever topic you opt for, you’d better start your preparation and research on the subject as early as possible.

Steps to Choose Your Ideal Capstone Project Topic

When selecting a topic, consider what truly interests you. Your passion for the subject will shine through in your work and keep you engaged throughout the project. It’s also crucial to choose a topic that aligns with current trends and your future career goals. This strategic approach ensures that your project is relevant and may even impress potential employers.

Here’s how to approach selecting your capstone topic:

Assess Personal Interests and Relevance to Trends:

• Think about the subjects you enjoy most and any current issues in your field that excite you.
• Are there hobbies or activities you are involved in that could inspire your project?
• Make sure your topic not only interests you but also connects with recent developments and trends in your field.

Consider Practicality and Available Resources:

• Evaluate the resources, time, and budget you can access for your project. Can you realistically complete your project with what you have?
• Consider if you have access to necessary data, equipment, and expert advice.

Consultation and Alignment with Career Goals:

• Talk about your ideas with advisors and mentors. They can offer valuable feedback on the practicality and relevance of your proposed topics.
• Your project should help you advance your career goals, so choose a topic that helps demonstrate your professional abilities and ambitions.

Set Clear Objectives and Assess Impact:

• Define what you aim to achieve with your capstone project. Whether it’s solving a specific problem, contributing new knowledge, or creating a practical solution, your goals should guide your research.
• Consider the potential impact of your project. Choose capstone ideas that offer practical applications and could significantly benefit your field or society.

Remember to consider the feasibility of your project ideas. Assess whether you have access to the necessary resources, data, and tools needed to execute your project effectively. Planning with these elements in mind will help ensure that you can realistically complete your capstone project successfully and on time.

15 Best Nursing Capstone Project Ideas

Studying nursing is challenging, as it requires a prominent theoretical foundation and is fully practical at the same time. You should have to do thorough research and provide evidence for your ideas, but what to start with? The preparation for your capstone project in nursing won’t be overwhelming if you use these capstone title ideas:

• Innovation and Improvement in Nursing
• Vaccination Chart Creation
• The Role of Nurses in Today’s Society
• Shortage in Nursing and Its Effects on Healthcare
• Evidential Practices and Their Promotion in Nursing
• Global Changes in the Approach to Vaccination
• Top Emergency Practices
• Preventive Interventions for ADHD
• Quality of Nursing and Hospital Personnel Shifts: The Interrelation
• Ways to Prevent Sexually Transmitted Diseases
• Brand New Approaches in Diagnostics in the Nursing Field
• Diabetes Mellitus in Young Adults: Prevention and Treatment
• Healthcare in Ambulances: Methods of Improvement
• Postpartum Depression Therapy
• The Ways to Carry a Healthy Baby

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15 Attractive Computer Science Capstone Project Ideas

Computer science is so rapidly developing that you might easily get lost in the new trends in the sphere. Gaming and internet security, machine learning and computer forensics, artificial intelligence, and database development – you first have to settle down on something. Check the topics for the capstone project examples below to pick one. Decide how deeply you will research the topic and define how wide or narrow the sphere of your investigation will be.

• Cybersecurity: Threats and Elimination Ways
• Data Mining in Commerce: Its Role and Perspectives
• Programming Languages Evolution
• Social Media Usage: How Safe Is It?
• Classification of Images
• Implementation of Artificial Intelligence in Insurance Cost Prediction
• Key Security Concerns of Internet Banking
• SaaS Technologies of the Modern Time
• The evolvement of Mobile Gaming and Mobile Gambling
• The Role of Cloud Computing and IoT in Modern Times
• Chatbots and Their Role in Modern Customer Support
• Computer Learning Hits and Misses
• Digitalization of Education
• Artificial Intelligence in Education: Perspectives
• Software Quality Control: Top Modern Practices

20 High School Education Capstone Project Ideas for Inspiration

High school education is a transit point in professional education and the most valuable period for personal soft skills development. As a result, high school capstone project ideas cover a wide range of topics. They may range from local startup analysis and engineer’s career path to bullying problems. It’s up to you to use the chosen statement as the ready capstone project title or just an idea for future development.

• A Small Enterprise Business Plan
• Advantages and Disadvantages of Virtual Learning in Schools
• Space Tourism: The Start and Development
• Pros and Cons of Uniforms and Dress Codes
• What is Cyberbullying and How to Reduce It
• Becoming a Doctor: Find Your Way
• A Career in Sports: Pros and Cons
• How to Eliminate the Risks of Peer Pressure
• Ensuring Better Behaviours in Classroom
• Cutting-Edge Technologies: NASA versus SpaceX
• The Reverse Side of Shyness
• Stress in High School and the Ways to Minimize It
• How to Bring Up a Leader
• Outdated Education Practices
• Learning Disabilities: What to Pay Attention to in Children’s Development
• The Impact of Early Childhood Education on Long-Term Academic Success
• Addressing the Achievement Gap in Public Schools
• Evaluating the Effectiveness of STEM Education Programs
• The Role of Parental Involvement in Student Achievement
• Inclusive Education: Strategies for Supporting Students with Disabilities

15 Capstone Project Topics in Information Technology – Search for Your Best

Information technology is a separate area developed on the basis of computer science, and it might be challenging to capture the differences between them. If you hesitate about what to start with – use the following topics for the capstone project as the starting point for your capstone research topics.

• Types of Databases in Information Systems
• Voice Recognition Technology and Its Benefits
• The Perspectives of Cloud Computing
• Security Issues of VPN Usage
• Censorship in Internet Worldwide
• Problems of Safe and Secure Internet Environment
• The Cryptocurrency Market: What Are the Development Paths?
• Analytics in the Oil and Gas Industry: The Benefits of Big Data Utilization
• Procedures, Strengths, and Weaknesses in Data Mining
• Networking Protocols: Safety Evaluation
• Implementation of Smart Systems in Parking
• Workplace Agile Methodology
• Manual Testing vs. Automated Testing
• Programming Algorithms and the Differences Between Them
• Strengths and Weaknesses of Cybersecurity
• Free unlimited checks
• All common file formats
• Accurate results
• Intuitive interface

15 Interesting Psychology Capstone Project Ideas

Society shows increasing attention to mental health. The range of issues influencing human psychology is vast, and the choice may be difficult. You’ll find simple capstone project ideas to settle on in the following list.

• The Impact of Abortion on Mental Health
• Bipolar Disorder and Its Overall Effects on the Life Quality
• How Gender Influences Depression
• Inherited and Environmental Effects on Hyperactive Children
• The Impact of Culture on Psychology
• How Sleep Quality Influences the Work Performance
• Long- and Short-Term Memory: The Comparison
• Studying Schizophrenia
• Terrorist’s Psychology: Comprehension and Treatment
• The Reasons for Suicidal Behaviour
• Aggression in Movies and Games and Its Effects on Teenagers
• Military Psychology: Its Methods and Outcomes
• The Reasons for Criminal Behavior: A Psychology Perspective
• Psychological Assessment of Juvenile Sex Offenders
• Do Colours Affect The Brain?

15 Capstone Project Ideas for Management Course

Studying management means dealing with the most varied spheres of life, problem-solving in different business areas, and evaluating risks. The challenge starts when you select the appropriate topic for your capstone project. Let the following list help you come up with your ideas.

• Innovative Approaches in Management in Different Industries
• Analyzing Hotels Customer Service
• Project Manager: Profile Evaluation
• Crisis Management in Small Business Enterprises
• Interrelation Between Corporate Strategies and Their Capital Structures
• How to Develop an Efficient Corporate Strategy
• The Reasons For Under-Representation of Managing Women
• Ways to Create a Powerful Public Relations Strategy
• The Increasing Role of Technology in Management
• Fresh Trends in E-Commerce Management
• Political Campaigns Project Management
• The Risk Management Importance
• Key Principles in the Management of Supply Chains
• Relations with Suppliers in Business Management
• Business Management: Globalization Impact

15 Capstone Project Ideas for Your Marketing Course

Marketing aims to make the business attractive to the customer and client-oriented. The variety of easy capstone project ideas below gives you the start for your research work.

• How to Maximize Customer Engagement
• Real Businesses Top Content Strategies
• Creation of Brand Awareness in Online Environments
• The Efficiency of Blogs in Traffic Generation
• Marketing Strategies in B2B and B2C
• Marketing and Globalization
• Traditional Marketing and Online Marketing: Distinguishing Features
• How Loyalty Programs Influence Customers
• The Principles of E-Commerce Marketing
• Brand Value-Building Strategies
• Personnel Metrics in Marketing
• Social Media as Marketing Tools
• Advertising Campaigns: The Importance of Jingles
• How to Improve Marketing Channels
• Habitual Buying Behaviours of Customers

15 Best Capstone Engineering Project Ideas

It’s challenging to find a more varied discipline than engineering. If you study it – you already know your specialization and occupational interest, but the list of ideas below can be helpful.

• How to Make a Self-Flying Robot
• How to Make Robotic Arm
• Biomass-fuelled Water Heater
• Geological Data: Transmission and Storage
• Uphill Wheelchairs: The Use and Development
• Types of Pollution Monitoring Systems
• Operation Principles of Solar Panels
• Developing a Playground for Children with Disabilities
• The Car with a Remote-Control
• Self-Driving Cars: Future or Fantasy?
• The Perspectives of Stair-Climbing Wheelchair
• Mechanisms of Motorized Chains
• How to Build a Car Engine
• Electric Vehicles are Environment-Friendly: Myth or Reality?
• The Use of Engineering Advancements in Agriculture

15 Senior Capstone Project Ideas for MBA

Here you might read some senior capstone project ideas to help you with your MBA assignment.

• Management Strategies for Developing Countries Businesses
• New App Market Analysis
• Corporate Downsizing and the Following Re-Organization
• How to Make a Business Plan for a Start-Up
• Relationships with Stakeholders
• Small Teams: Culture and Conflict
• Organization Managing Diversity
• What to Pay Attention to in Business Outsourcing
• Business Management and Globalization
• The Most Recent HR Management Principles
• Dealing with Conflicts in Large Companies
• Culturally Differentiated Approaches in Management
• Ethical Principles in Top-Tier Management
• Corporate Strategy Design
• Risk Management and Large Businesses

15 Capstone Project Ideas for an Accounting Course

Try these ideas for your Capstone Project in Accounting – and get the best result possible.

• How Popular Accounting Theories Developed
• Fixed Assets Accounting System
• Accounting Principles in Information Systems
• Interrelation Between Accounting and Ethical Decision-Making
• Ways to Minimize a Company’s Tax Liabilities
• Tax Evasion and Accounting: Key Principles
• Auditing Firm Accounting Procedures
• A New Accounting Theory Development
• Accounting Software
• Top Three World Recessions
• Accounting Methods in Proprietorship
• Accounting Standards Globally and Locally
• Personal Finance and the Recession Effect
• Company Accounting: Managerial Principles and Functions
• Payroll Management Systems

10 Environmental Science Capstone Project Ideas

Here are ten innovative capstone project ideas in Environmental Science. They address pressing ecological challenges and promote sustainable practices:

• Assessing the Impact of Plastic Waste on Marine Life
• Urban Heat Islands: Mitigation Strategies for Cities
• Renewable Energy Adoption in Rural Areas
• Conservation Strategies for Endangered Species
• Evaluating the Effectiveness of National Parks in Biodiversity Preservation
• Sustainable Agriculture Practices for Reducing Carbon Footprint
• The Role of Wetlands in Climate Change Mitigation
• Analysis of Water Quality in Local Rivers and Lakes
• Impact of Urban Development on Local Wildlife
• Strategies for Reducing Air Pollution in Urban Areas

10 Public Health Capstone Project Ideas

Here are ten capstone project topics in Public Health. These ideas will help students study and better understand important health issues in their communities:

• Community-Based Approaches to Combat Obesity
• Strategies to Increase Vaccination Rates in Underserved Populations
• Evaluating Mental Health Services in Rural Communities
• Reducing Substance Abuse Among Adolescents
• Impact of Housing Conditions on Health Outcomes
• Public Health Education Campaigns for Preventing Heart Disease
• Assessing the Effectiveness of Smoking Cessation Programs
• Addressing Health Disparities in Minority Populations
• Implementing Telehealth Solutions for Chronic Disease Management
• Improving Access to Maternal Healthcare Services

10 Political Science Capstone Project Ideas

• The Impact of Social Media on Political Campaigns
• Voter Turnout: Strategies to Increase Participation
• Analyzing the Effectiveness of Lobbying in Policy Making
• The Role of International Organizations in Global Governance
• Electoral Reforms: Comparative Analysis of Different Countries
• Public Opinion and Its Influence on Government Policy
• The Effect of Political Polarization on Legislative Processes
• Human Rights Violations: Case Studies and Policy Recommendations
• The Role of Grassroots Movements in Political Change
• Analyzing the Effectiveness of Environmental Policies

10 Best Capstone Project Ideas in Economics

Here are ten capstone project topics in economics. They will help you to explore and analyze key economic issues and trends.

• The Impact of Minimum Wage Increases on Small Businesses
• Analyzing Income Inequality in Urban Areas
• The Economics of Renewable Energy Adoption
• Evaluating the Effects of Trade Policies on Local Economies
• The Role of Microfinance in Alleviating Poverty
• Assessing the Economic Impact of Immigration
• The Future of Work: Automation and Job Displacement
• Analyzing the Effects of Tax Reform on Economic Growth
• Behavioral Economics: Nudging Towards Better Financial Decisions
• The Economics of Healthcare Access and Affordability

10 Sociology Capstone Project Ideas

• The Impact of Social Media on Youth Identity Formation
• Analyzing the Effects of Urbanization on Community Life
• Gender Roles in Modern Society: Shifts and Challenges
• The Influence of Family Dynamics on Educational Attainment
• Social Movements and Their Impact on Policy Change
• The Role of Religion in Shaping Social Norms
• Studying Homelessness: Causes and Solutions
• The Effects of Social Inequality on Mental Health
• Racial and Ethnic Identity in Multicultural Societies
• The Influence of Pop Culture on Social Values

Capstone Writing: 10 Essential Steps

Be it a senior capstone project of a high school pupil or one for college, you follow these ten steps. This will ensure you’ll create a powerful capstone paper in the outcome and get the best grade:

• One of the tips for choosing a topic that your professors would be interested in is picking a subject in the course of your classes. Make notes during the term, and you will definitely encounter an appropriate topic.
• Opt for a precise topic rather than a general one. This concerns especially business subjects.
• Have your capstone project topic approved by your professor.
• Conduct a thorough information search before developing a structure.
• Don’t hesitate to do surveys; they can provide extra points.
• Schedule your time correctly, ensuring a large enough time gap for unpredictable needs.
• Never avoid proofreading – this is the last but not least step before submission.
• Stick up to the topic and logical structure of your work.
• Get prepared to present your project to the audience, learn all the essential points, and stay confident.
• Accept feedback open-mindedly from your teacher as well as your peers.

Preparation of a powerful capstone project involves both selection of an exciting topic and its in-depth examination. If you are interested in the topic, you can demonstrate a deep insight into the subject to your professor. The lists of ideas above will inspire you and prepare you for the successful completion of your project. Don’t hesitate to try them now!

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Capstone Project in Data Science

(Fall 2020, Winter 2021, Spring 2021)

The course will study data science from the systems engineering perspective, introduce and address a variety of ethical issues that arise in data science projects, and engage students in project-based learning through a series of carefully selected and curated data science studies. A major overarching goal is to prepare students to make a positive impact on the world with data- intensive methodologies. In line with this, we will study and discuss a number of case studies in “ethics in data science” which emphasize responsible data practice. Another major focus will be on correctly interpreting, explaining, and communicating the results of analyses. This component of the course will focus on decision making under uncertainty, the role of correlation and causation, and drawing attention to common statistical traps and paradoxes that drive erroneous conclusions.

The Fall course is a lecture-based course with projects and papers. The capstone projects (pursued in Winter and Spring) will be interdisciplinary, will have outside customers, and will require students to apply skills or investigate issues across different subject areas or domains of knowledge. Students will work with leaders from the industry and research labs. See the sponsoring institutions at https://centralcoastdatascience.org/industry . Examples of projects include quantifying insect-plant network interactions, risk prediction, energy efficiency, inferring health from personal fitness devices, call tracking/analytics, and modeling of COVID-19.

Upon completing the course sequence, students will be able to understand the data science process and the structure and the role of each of its constituent steps; engineer the appropriate data science process for a given data analytical problem; design and implement evaluation studies to compare the quality of performed data analysis; understand technical trade-offs associated with working with “Big Data”; understand ethical implications of data science work, and be able to apply ethical reasoning to specific data science projects; visualize the results of data analytical studies, and convey them to customers.

• Classroom instruction in Fall 2020: focus on the process of discovering knowledge from data, public policy, ethics, fairness, and statistical traps.
• Followed by two quarters of faculty-mentored experiential project work. 
• Synthesize course materials from individual machine learning, statistics, and data engineering courses, and place them in the context of concrete problems and datasets.
• Culminates in an end-of-year showcase of projects to the local data science community
• Oral communication and public speaking
• Time management
• Data analysis and informed decision making

Enrollment details: 4 units each quarter

• Fall 2020: CMPSC 190DD, MW 5-6:30.
• Winter 2021: CMPSC 190DE, times TBD.
• Spring 2021: CMPSC 190DF, times TBD.

If you are interested, please fill out this course survey.  

Staff: Tim Robinson, [email protected] Faculty: Ambuj Singh, [email protected]

UCSB Contact

• +1-805-893-3510
• [email protected]
• Office 2118 Harold Frank Hall

Cal Poly Contact

• +1-805-756-5624
• [email protected]
• Office 14-227
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Best Capstone Project Ideas for Students across subjects

Updated 02 Sep 2024

The most challenging aspect of crafting a top-tier capstone project is often getting started. The initial hurdle involves selecting a strong, impactful topic that aligns with your strengths and academic goals. A well-chosen topic not only highlights your potential but also sets the foundation for a successful project. Conversely, a weak topic can lead to a less effective outcome. To assist you in this crucial step, we’ve compiled a list of innovative high school senior capstone ideas and capstone project examples to guide you toward the right choice.

Understanding Capstone Projects: Purpose and Application

A capstone project is a culminating academic experience typically undertaken during the final phase of a degree program. Capstone project topics span various fields, such as economics, public health, and information technology, emphasizing the importance of selecting relevant and innovative themes for academic projects. It allows students to demonstrate the knowledge and skills they’ve acquired throughout their coursework by tackling a real-world problem or challenge within their field of study. Capstone projects are expected in undergraduate and graduate programs, especially in disciplines like engineering, business, nursing, and information technology.

These projects are often required in educational settings, including universities and professional schools, as a means to integrate and apply theoretical knowledge in a practical context. Students usually work independently or in groups under the guidance of a faculty advisor, with the project often serving as a bridge between academic learning and professional practice. The results of a capstone project can take various forms, such as a research paper, a presentation, or a physical product, and are typically presented to a panel of faculty members or industry professionals.

How to Choose the Perfect Capstone Project Topic

Selecting a topic for your capstone project is a critical step in setting the foundation for your academic endeavor. The right topic will allow you to showcase the knowledge and skills you’ve developed throughout your studies while addressing a real-world problem. To start, brainstorm ideas that are relevant to your field and spark your interest. This personal connection can be key to maintaining motivation throughout the project.

Consider exploring innovative capstone project ideas, especially those that tackle urgent ecological issues and encourage sustainable practices.

Next, narrowing down your ideas by reviewing the existing literature is essential. This step will help you identify gaps in current research or practice, allowing you to contribute something new and valuable to your field. A topic that is too broad can become overwhelming, so aim for a specific issue that is manageable within the scope of your project.

Finally, seek feedback from your advisor or peers to refine your topic choice. Their insights can help you avoid potential pitfalls and ensure that your topic is both challenging and achievable. By carefully selecting a well-defined, relevant, and interesting topic, you’ll set yourself up for a successful capstone project that truly reflects your academic achievements.

Capstone Project Ideas for Students

Exploring our curated list of top high school senior capstone ideas can provide valuable inspiration if you're about to embark on your capstone project. These examples from the  capstone project writing service EduBirdie offer a solid starting point for selecting a topic that aligns with your interests and academic goals. For students interested in cybersecurity, delving into specialized cybersecurity capstone project ideas within this field can be particularly rewarding, as they provide opportunities to apply theoretical knowledge to real-world scenarios in a rapidly growing industry.

Capstone Engineering Project Ideas

• Renewable Energy from Ocean Waves
• Automated Irrigation System
• 3D Printed Prosthetics
• Smart Traffic Management System
• Earthquake-Resistant Building Design
• Solar-Powered Water Purification
• Wind Turbine Optimization
• Autonomous Drone Delivery
• Green Building Design
• Smart Home Automation
• Electric Vehicle Charging Station
• Waste-to-Energy Conversion
• Hydroelectric Power Model
• Intelligent Transportation System
• Noise Pollution Control
• Self-Healing Concrete
• Low-Cost Ventilator Design
• Advanced Water Desalination
• Bridge Structural Analysis
• Smart Grid Implementation

Nursing Capstone Project Ideas

• Improving Patient Safety Protocols
• Telehealth Solutions for Rural Areas
• Pain Management in Post-Operative Care
• Reducing Hospital-Acquired Infections
• Enhancing Communication in Critical Care
• Mental Health Support for Nurses
• Fall Prevention Programs
• Improving Medication Administration Accuracy
• Promoting Healthy Lifestyles for the Elderly
• Palliative Care for Cancer Patients
• Nutritional Support for Diabetic Patients
• Stress Management for Nursing Staff
• Increasing Vaccination Rates in Pediatrics
• Chronic Disease Prevention Strategies
• Wound Care Management at Home
• Heart Disease Patient Education Programs
• Reducing Nurse Burnout with Mindfulness
• End-of-Life Care Improvement in Nursing Homes
• Postpartum Depression Screening and Support
• Developing Pain Assessment Tools for Non-Verbal Patients

Information Technology Capstone Project Ideas

• Cybersecurity Threat Detection System
• Blockchain-Based Voting System
• AI-Powered Customer Support Chatbot
• Cloud Data Backup and Recovery System
• Smart Inventory Management System
• IoT-Based Home Security System
• E-Commerce Website Development
• Mobile App for Smart Cities
• Online Learning Management System
• Virtual Reality Training Simulator
• AI-Based Image Recognition System
• Business Intelligence Data Analytics Dashboard
• Social Media Sentiment Analysis Tool
• Augmented Reality Shopping Experience
• IoT Environmental Monitoring System
• Machine Learning Recommendation System
• Cybersecurity Awareness Training Platform
• Healthcare Data Management System
• Online Exam System with Anti-Cheating
• Voice-Activated Personal Assistant App

Computer Science Capstone Project Ideas

• AI-Based Text Summarization Tool
• Real-Time Language Translation App
• Blockchain Secure Document Sharing
• Face Recognition Attendance System
• AI Predictive Maintenance System
• Virtual Reality Game Development
• Smart Personal Assistant with Voice Commands
• Real-Time Traffic Analysis Using Computer Vision
• Automated Code Review Tool
• Cloud-Based Online IDE for Coding
• AI-Based Video Editing Software
• Deep Learning Image Classification
• Interactive Virtual Tour System
• Smart Contract Development on Ethereum
• AI Health Diagnosis System
• Chatbot for Online Customer Service
• Machine Learning Stock Market Prediction
• Secure Mobile Payment System
• NLP for Sentiment Analysis
• AI Content Recommendation Engine

MBA Capstone Project Ideas<

• Digital Marketing Strategy for Startups
• Financial Analysis of Mergers & Acquisitions
• Business Process Optimization in Manufacturing
• Customer Retention Strategies for E-Commerce
• Sustainable Business Practices in Retail
• Impact of CSR on Brand Loyalty
• Market Entry Strategy for New Products
• Analysis of Supply Chain Management
• Franchise Business Model Development
• Globalization Impact on Small Businesses
• Financial Risk Management in Banking
• HR Strategies for Remote Work
• Brand Positioning and Competitive Analysis
• Business Plan for a Social Enterprise
• Consumer Behavior in the Digital Age
• Innovation Management in Tech Companies
• Corporate Governance and Ethical Practices
• Digital Transformation in Traditional Businesses
• CRM System Development
• Strategic Planning for Business Expansion

Accounting Capstone Project Ideas

• Impact of IFRS Adoption on Financial Reporting
• Forensic Accounting Techniques for Fraud Detection
• Cost-Benefit Analysis of Corporate Social Responsibility
• Tax Planning Strategies for Small Businesses
• Financial Analysis of Mergers and Acquisitions
• Role of Auditing in Corporate Governance
• Effectiveness of Internal Controls in Fraud Prevention
• Technological Advancements in Accounting
• Valuation Methods for Startups
• Sustainability Reporting Impact on Investor Decisions
• Financial Risk Management in Multinational Corporations
• Comparative Analysis of Financial Ratios Across Industries
• Economic Recession Impact on Corporate Financial Performance
• Big Data’s Role in Modern Accounting
• Ethical Issues in Financial Reporting
• Cash Flow Management in Non-Profits
• Budgeting and Forecasting in the Public Sector
• Digital Currencies Impact on Financial Reporting
• Cost Accounting in Manufacturing
• Impact of AI and Automation on the Future of Accounting

Management Capstone Project Ideas

• Leadership Styles and Their Impact on Employee Performance
• Change Management in Large Organizations
• Employee Engagement Strategies for Remote Work
• Crisis Management Planning in the Hospitality Industry
• Diversity and Inclusion Strategy Development
• Strategic Planning for Business Growth
• The Role of Corporate Culture in Business Success
• Risk Management in Project Management
• Effective Succession Planning Process
• Technology's Impact on Modern Management Practices
• Improving Decision-Making with Data Analytics
• Sustainable Management Practices in Retail
• Conflict Resolution Strategies in the Workplace
• HR Management in Multinational Corporations
• Communication Strategies in Management
• Impact of Mergers and Acquisitions on Employee Morale
• Supply Chain Management Effectiveness
• Developing Strategic Management Frameworks for SMEs
• Implementing Work-Life Balance Programs
• The Role of Innovation in Competitive Advantage

Education Capstone Project Ideas

• Impact of Technology on Student Engagement
• Teacher Training for Inclusive Education
• Developing Effective Special Education Programs
• Analyzing Online Learning Platform Effectiveness
• Strategies for Reducing the Achievement Gap
• The Role of Parental Involvement in Student Success
• Curriculum Development for Multicultural Classrooms
• Addressing Mental Health Issues in Schools
• Class Size Impact on Learning Outcomes
• Implementing STEM Education in Early Childhood
• Strategies for Bullying Prevention in Schools
• Impact of Standardized Testing on Education
• Teacher Retention Strategies in Urban Schools
• Project-Based Learning Implementation in High Schools
• The Role of Arts Education in Holistic Development
• Improving Literacy Rates Through Community Programs
• Supporting English Language Learners in Schools
• Socioeconomic Status Impact on Educational Outcomes
• Developing School Safety Plans
• Evaluating Teacher Evaluation Systems

Marketing Capstone Project Ideas

• Impact of Social Media Marketing on Brand Loyalty
• Developing a Digital Marketing Strategy for E-Commerce
• Consumer Behavior Analysis in the Fashion Industry
• The Role of Influencer Marketing in Brand Promotion
• Market Segmentation for New Product Launches
• Content Marketing’s Effect on Customer Engagement
• Brand Positioning Strategies for Startups
• Effectiveness of Email Marketing Campaigns
• Role of Big Data in Personalized Marketing
• Cultural Differences Impact on Global Marketing
• Customer Retention in Subscription Services
• Ethical Considerations in Advertising
• Impact of Pricing Strategies on Consumer Perception
• Sustainable Marketing Strategy Development
• Effectiveness of Guerrilla Marketing Tactics
• Mobile Marketing’s Influence on Consumer Behavior
• Role of Public Relations in Crisis Management
• Social Media Content Strategy for B2B Companies
• Video Marketing’s Impact on Consumer Engagement
• Virtual Reality in Experiential Marketing

High School Capstone Project Ideas

• Exploring Renewable Energy Solutions for Schools
• Designing an Anti-Bullying Campaign
• The Impact of Social Media on Teen Mental Health
• Developing a Community Garden for Sustainable Living
• Creating a High School Recycling Program
• The Role of Music in Cognitive Development
• Exploring the Effects of Sleep Deprivation on Students
• Developing a Peer Tutoring Program
• Analyzing the Impact of Technology on Study Habits
• Creating a School Safety Plan
• Investigating Local Water Quality
• Designing a Mobile App for School Events
• The Impact of School Uniforms on Student Behavior
• Exploring Historical Events Through Virtual Reality
• Analyzing the Effects of Nutrition on Academic Performance
• Developing a Mental Health Awareness Campaign
• Studying the Impact of Extracurricular Activities on Student Success
• Designing an Eco-Friendly Transportation Plan for Students
• Exploring the Effects of Video Games on Cognitive Skills
• Creating a Financial Literacy Program for Teens

Capstone projects provide students with a valuable opportunity to apply their academic knowledge to real-world problems, effectively bridging the gap between theory and practice. Whether in fields like engineering, nursing, information technology, or business management, these projects demand creativity, critical thinking, and problem-solving skills. Students demonstrate their expertise by addressing complex challenges—such as renewable energy, telehealth implementation, or digital marketing strategies—and contribute innovative solutions to their industries.

Capstone projects culminate students' academic journeys, enabling them to integrate and apply their knowledge in practical ways. Whether focused on technology development, educational improvement, or business optimization, these projects empower students to significantly contribute to their fields, preparing them for professional success and advancing industry practices.

However, tackling a capstone project is a complex task that not everyone can manage alone. If you're struggling, law essay writing service EduBirdie is here to help you succeed, ensuring that your hard work results in the highest grades possible.

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Written by David Kidwell

David is one of those experienced content creators from the United Kingdom who has a high interest in social issues, culture, and entrepreneurship. He always says that reading, blogging, and staying aware of what happens in the world is what makes a person responsible. He likes to learn and share what he knows by making things inspiring and creative enough even for those students who dislike reading.

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