applied data science with python assignment 2

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Applied Data Science with Python

Description.

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

U-M Credit Eligible

Instructors.

Christopher Brooks

Associate Professor of Information

School of Information

Kevyn Collins-Thompson

Associate Professor

Daniel Romero

Assistant Professor

V.G. Vinod Vydiswaran

Courses (5)

Introduction to Data Science in Python

Applied Plotting, Charting & Data Representation in Python

Applied Machine Learning in Python

Applied Text Mining in Python

Applied Social Network Analysis in Python

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• Description
• Announcements
• Class Logistics

Live Session Plan

• Assignments and Final Project Submission Guidelines

DataSci W207: Applied Machine Learning

Lecture: mo, wed, th, office hours: tu, 8-9 am pt.

This course provides a practical introduction to the rapidly growing field of machine learning— training predictive models to generalize to new data. We start with linear and logistic regression and implement gradient descent for these algorithms, the core engine for training. With these key building blocks, we work our way to understanding widely used neural network architectures, focusing on intuition and implementation with TensorFlow/Keras. While the course centers on neural networks, we will make sure to cover key ideas in unsupervised learning and nonparametric modeling.

Along the way, weekly short coding assignments will connect lectures with concrete data and real applications. A more open-ended final project will tie together crucial concepts in experimental design and analysis with models and training.

This class meets for one 90 min class periods each week.

All materials for this course are posted on GitHub in the form of Jupyter notebooks.

• Please fill out this PRE-COURSE survey so I can get to know a bit more about you and your programming background.
• Due to a large number of private Slack inquiries, I encourage you to first read this website for commonly asked questions.
• Any questions regarding course content and organization (including assignments and final project) should be posted on my Slack channel. You are strongly encouraged to answer other students' questions when you know the answer.
• If there are private matters specific to you (e.g., special accommodations), please contact me directly.
• If you miss a class, watch the recording and inform me here .
• If you want to stay up to date with recent work in AI/ML, start by looking at the conferences NeurIPS and ICML .
• ML study guidelines: Stanford's super cheatsheet .

Core data science courses: research design, storing and retrieving data, exploring and analyzing data.

Undergraduate-level probability and statistics. Linear algebra is recommended.

Python (v3).

Jupiter and JupiterLab notebooks. You can install them in your computer using pip or Anaconda . More information here .

Git(Hub), including clone/commmit/push from the command line. You can sign up for an account here.

If you have a MacOS M1, this .sh script will install everything for you (credit goes to one of my former students, Michael Tay)

Mac/Windows/Linux are all acceptable to use.

• Raschka & Mirjalili (RM) , Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2.
• Weekly coding assignments, submitted via GitHub and Digital Campus (see notes below).
• You will present your final project in class during the final session. You are allowed to work in teams.
• You will submmit your code and presentation slides via GitHub and Digital Campus (see notes below).

Communication channel

For the final project you will form a group (3-4 people are ideal; 2-5 people are allowed; no 1 person group allowed). Grades will be calibrated by group size. Your group can only include members from the section in which you are enrolled.

Do not just re-run an existing code repository; at the minimum, you must demonstrate the ability to perform thoughtful data preprocessing and analysis (e.g., data cleaning, model training, hyperparameter selection, model evaluation).

The topic of your project is totally flexible (see also below some project ideas).

• week 04: inform me here about your group, question and dataset you plan to use.
• week 08: prepare the baseline presentation of your project. You will present in class (no more than 10 min).
• week 16: prepare the final presentation of your project. You will present in class (no more than 20 min).
• Can we predict solar panel electricity production using equipment and weather data?
• Predict Stock Portfolio Returns using News Headlines
• Pneumonia Detection from Chest Xrays
• Predicting Energy Usage from Publically Available Building Performance Data
• Can we Predict What Movies will be Well Received?
• ML for Music Genre Classification
• Predicting Metagenome Sample Source Environment from Protein Annotations
• California Wildfire Prediction
• Title, Authors
• What is the question you will be working on? Why is it interesting?
• What is the data you will be using? Include data source, size of dataset, main features to be used. Please also include summary statistics of your data.
• What prediction algorithms do you plan to use? Please describe them in detail.
• How will you evaluate your results? Please describe your chosen performance metrices and/or statistical tests in detail.
• (15%) Motivation: Introduce your question and why the question is interesting. Explain what has been done before in this space. Describe your overall plan to approach your question. Provide a summary of your results.
• (15%) Data: Describe in detail the data that you are using, including the source(s) of the data and relevant statistics.
• (15%) Approach: Describe in detail the models (baseline + improvement over baseline) that you use in your approach.
• (30%) Experiments: Provide insight into the effect of different hyperperameter choices. Please include tables, figures, graphs to illustrate your experiments.
• (10%) Conclusions: Summarize the key results, what has been learned, and avenues for future work.
• (15%) Code submission: Provide link to your GitHub repo. The code should be well commented and organized.
• Contributions: Specify the contributions of each author (e.g., data processing, algorithm implementation, slides etc).
• Step 1: Create GitHub repos for Assignments 1-10 and Final Project
• Step 2: If weekly assignments, upload .ipynb file in Gradescope. If final project, upload an .ipynb file that contains the link to your group GitHub repo (add your presentation slides to the repo; each team member submits in Gradescope)

Final grades will be determined by computing the weighted average of programming projects, final group project, and participation.

Baseline grading range for this course is: A for 93 or above, A- for 90 or above, B+ for 87 or above, B for 83 or above, B- for 80 or above, C+ for 77 or above, C for 73 or above, C- for 70 and above, D+ for 67 and above, D for 63 and above, D- for 60 and above, and F for 59 and below.

Integrating a diverse set of experiences is important for a more comprehensive understanding of machine learning. I will make an effort to read papers and hear from a diverse group of practitioners, still, limits exist on this diversity in the field of machine learning. I acknowledge that it is possible that there may be both overt and covert biases in the material due to the lens with which it was created. I would like to nurture a learning environment that supports a diversity of thoughts, perspectives and experiences, and honors your identities (including race, gender, class, sexuality, religion, ability, veteran status, etc.) in the spirit of the UC Berkeley Principles of Community.

To help accomplish this, please contact me or submit anonymous feedback through I School channels if you have any suggestions to improve the quality of the course. If you have a name and/or set of pronouns that you prefer I use, please let me know. If something was said in class (by anyone) or you experience anything that makes you feel uncomfortable, please talk to me about it. If you feel like your performance in the class is being impacted by experiences outside of class, please don’t hesitate to come and talk with me. I want to be a resource for you. Also, anonymous feedback is always an option, and may lead to me to make a general announcement to the class, if necessary, to address your concerns.

As a participant in teamwork and course discussions, you should also strive to honor the diversity of your classmates.

If you prefer to speak with someone outside of the course, MICS Academic Director Lisa Ho, I School Assistant Dean of Academic Programs Catherine Cronquist Browning, and the UC Berkeley Office for Graduate Diversity are excellent resources. Also see the following link.

Instantly share code, notes, and snippets.

GeorgyGol / Assignment2 (1).ipynb

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the-egg4eva commented Feb 2, 2020

Sorry, something went wrong.

elifapa commented Apr 26, 2020

I didnt quite get where you used the listdata() function in your plotting.

Introduction to Applied Data Science with Python

Free data science with python course with certificate.

Begin your Data Science journey with this data science with Python course for free, where you'll learn Python basics and its application in Data Science. Explore libraries like NumPy and Pandas for data analysis and gain insights into linear algebra, statistics, and probability. Whether stepping into this field or aiming to enhance your existing skills, this course is the right pathway.

• 2 Hours Of self-paced video lessons
• Completion Certificate awarded on course completion
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Data Science with Python Skills you will learn

• Python programming concepts
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Who should learn this free Data Science with Python course?

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Introduction, lesson 1 : introduction to data science, introduction to data science, lesson 2 : basics of python programming, basics of python programming, lesson 3 : numpy in python, numpy in python, lesson 4 : linear algebra in data science, linear algebra in data science, lesson 5 : statistics and probability in data science, statistics and probability in data science, lesson 6 : pandas in python, pandas in python, lesson 7 : data analysis with python, data analysis with python, lesson 8 : data wrangling in python, data wrangling in python, lesson 9 : data visualization with python, data visualization with python, get a completion certificate.

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Why is Python popular in data science?

Python's popularity in data science stems from its simplicity, readability, and extensive ecosystem of libraries. It offers powerful tools like Pandas, NumPy, and Matplotlib, simplifying data manipulation, analysis, and visualization tasks.

Are there any prerequisites to enroll in this Data Science with Python course?

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While the data science with Python course free duration is 2 hours, the time to complete may vary based on individual learning pace and engagement with the material. It's designed to be flexible, accommodating learners of all levels.

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Learn Data Science Tutorial With Python

This Data Science Tutorial with Python tutorial will help you learn the basics of Data Science along with the basics of Python according to the need in 2024 such as data preprocessing, data visualization, statistics, making machine learning models, and much more with the help of detailed and well-explained examples. This tutorial will help beginners and trained professionals master data science with Python.

• What is Data Science

Data science is an interconnected field that involves the use of statistical and computational methods to extract insightful information and knowledge from data. Python is a popular and versatile programming language, now has become a popular choice among data scientists for its ease of use, extensive libraries, and flexibility. Python programming language provide and efficient and streamlined approach to handing complex data structure and extracts insights.

Table of Content

Introduction

Python basics, data analysis and processing, statistics for data science, supervised learning, unsupervised learning, natural language processing, how to learn data science, applications of data science, career opportunities in data science, faqs on data science tutorial, q.1 what is data science, q.2 what’s the difference between data science and data analytics , q.3 is python necessary for data science , geeksforgeeks courses.

Related Courses: Machine Learning is an essential skill for any aspiring data analyst and data scientist, and also for those who wish to transform a massive amount of raw data into trends and predictions. Learn this skill today with Machine Learning Foundation – Self Paced Course , designed and curated by industry experts having years of expertise in ML and industry-based projects.

• Introduction to Data Science
• What is Data?
• Python for Data Science
• Python Pandas
• Python Numpy
• Python Scikit-learn
• Python Matplotlib
• Taking input in Python
• Python | Output using print() function
• Variables, expression condition and function
• Basic operator in python
• Loops and Control Statements (continue, break and pass) in Python
• else with for
• Functions in Python
• Yield instead of Return
• Python OOPs Concepts
• Exception handling

For more information refer to our Python Tutorial

• Understanding Data Processing
• Python: Operations on Numpy Arrays
• Overview of Data Cleaning
• Slicing, Indexing, Manipulating and Cleaning Pandas Dataframe
• Working with Missing Data in Pandas
• Python | Read CSV
• Export Pandas dataframe to a CSV file
• Pandas | Parsing JSON Dataset
• Exporting Pandas DataFrame to JSON File
• Working with excel files using Pandas
• Connect MySQL database using MySQL-Connector Python
• Python: MySQL Create Table
• Python MySQL – Insert into Table
• Python MySQL – Select Query
• Python MySQL – Update Query
• Python MySQL – Delete Query
• Python NoSQL Database
• Python Datetime
• Data Wrangling in Python
• Pandas Groupby: Summarising, Aggregating, and Grouping data
• What is Unstructured Data?
• Label Encoding of datasets
• One Hot Encoding of datasets
• Data Visualization using Matplotlib
• Style Plots using Matplotlib
• Line chart in Matplotlib
• Bar Plot in Matplotlib
• Box Plot in Python using Matplotlib
• Scatter Plot in Matplotlib
• Heatmap in Matplotlib
• Three-dimensional Plotting using Matplotlib
• Time Series Plot or Line plot with Pandas
• Python Geospatial Data
• Data Visualization with Python Seaborn
• Using Plotly for Interactive Data Visualization in Python
• Interactive Data Visualization with Bokeh
• Measures of Central Tendency
• Statistics with Python
• Measuring Variance
• Normal Distribution
• Binomial Distribution
• Poisson Discrete Distribution
• Bernoulli Distribution
• Exploring Correlation in Python
• Create a correlation Matrix using Python
• Pearson’s Chi-Square Test
• Types of Learning – Supervised Learning
• Getting started with Classification
• Types of Regression Techniques
• Classification vs Regression
• Introduction to Linear Regression
• Implementing Linear Regression
• Univariate Linear Regression
• Multiple Linear Regression
• Python | Linear Regression using sklearn
• Linear Regression Using Tensorflow
• Linear Regression using PyTorch
• Pyspark | Linear regression using Apache MLlib
• Boston Housing Kaggle Challenge with Linear Regression
• Polynomial Regression ( From Scratch using Python )
• Polynomial Regression
• Polynomial Regression for Non-Linear Data
• Polynomial Regression using Turicreate
• Understanding Logistic Regression
• Implementing Logistic Regression
• Logistic Regression using Tensorflow
• Softmax Regression using TensorFlow
• Softmax Regression Using Keras
• Naive Bayes Classifiers
•  Naive Bayes Scratch Implementation using Python
• Complement Naive Bayes (CNB) Algorithm
• Applying Multinomial Naive Bayes to NLP Problems
• Support Vector Machine Algorithm
• Support Vector Machines(SVMs) in Python
• SVM Hyperparameter Tuning using GridSearchCV
• Creating linear kernel SVM in Python
• Major Kernel Functions in Support Vector Machine (SVM)
• Using SVM to perform classification on a non-linear dataset
• Decision Tree
• Implementing Decision tree
• Decision Tree Regression using sklearn
• Random Forest Regression in Python
• Random Forest Classifier using Scikit-learn
• Hyperparameters of Random Forest Classifier
• Voting Classifier using Sklearn
• Bagging classifier
• K Nearest Neighbors with Python | ML
• Implementation of K-Nearest Neighbors from Scratch using Python
• K-nearest neighbor algorithm in Python
• Implementation of KNN classifier using Sklearn
• Imputation using the KNNimputer()
• Implementation of KNN using OpenCV
• Types of Learning – Unsupervised Learning
• Clustering in Machine Learning
• Different Types of Clustering Algorithm
• K means Clustering – Introduction
• Elbow Method for optimal value of k in KMeans
• K-means++ Algorithm
• Analysis of test data using K-Means Clustering in Python
• Mini Batch K-means clustering algorithm
• Mean-Shift Clustering
• DBSCAN – Density based clustering
• Implementing DBSCAN algorithm using Sklearn
• Fuzzy Clustering
• Spectral Clustering
• OPTICS Clustering
• OPTICS Clustering Implementing using Sklearn
• Hierarchical clustering (Agglomerative and Divisive clustering)
• Implementing Agglomerative Clustering using Sklearn
• Gaussian Mixture Model
• Introduction to Deep Learning
• Introduction to Artificial Neutral Networks
• Implementing Artificial Neural Network training process in Python
• A single neuron neural network in Python
• Introduction to Convolution Neural Network
• Introduction to Pooling Layer
• Introduction to Padding
• Types of padding in convolution layer
• Applying Convolutional Neural Network on mnist dataset
• Introduction to Recurrent Neural Network
• Recurrent Neural Networks Explanation
• seq2seq model
• Introduction to Long Short Term Memory
• Long Short Term Memory Networks Explanation
• Gated Recurrent Unit Networks(GAN)
• Text Generation using Gated Recurrent Unit Networks
• Introduction to Generative Adversarial Network
• Generative Adversarial Networks (GANs)
• Use Cases of Generative Adversarial Networks
• Building a Generative Adversarial Network using Keras
• Modal Collapse in GANs
• Introduction to Natural Language Processing
• Text Preprocessing in Python | Set – 1
• Text Preprocessing in Python | Set 2
• Removing stop words with NLTK in Python
• Tokenize text using NLTK in python
• How tokenizing text, sentence, words works
• Introduction to Stemming
• Stemming words with NLTK
• Lemmatization with NLTK
• Lemmatization with TextBlob
• How to get synonyms/antonyms from NLTK WordNet in Python?

Usually, There are four areas to master data science.

• Industry Knowledge : Domain knowledge in which you are going to work is necessary like If you want to be a data scientist in Blogging domain so you have much information about blogging sector like SEOs, Keywords and serializing. It will be beneficial in your data science journey.
• Models and logics Knowledge: All machine learning systems are built on Models or algorithms, its important prerequisites to have a basic knowledge about models that are used in data science.
• Computer and programming Knowledge : Not master level programming knowledge is required in data science but some basic like variables, constants, loops, conditional statements, input/output, functions.
• Mathematics Used : It is an important part in data science. There is no such tutorial presents but you should have knowledge about the topics : mean, median, mode, variance, percentiles, distribution, probability, bayes theorem and statistical tests like hypothesis testing, Anova, chi squre, p-value.

Data science is used in every domain.

• Healthcare : Healthcare industries uses the data science to make instruments to detect and cure disease.
• Image Recognition : The popular application is identifying pattern in images and finds objects in image.
• Internet Search : To show best results for our searched query search engine use data science algorithms. Google deals with more than 20 petabytes of data per day. The reason google is a successful engine because it uses data science.
• Advertising : Data science algorithms are used in digital marketing which includes banners on various websites, billboard, posts etc. those marketing are done by data science. Data science helps to find correct user to show a particular banner or advertisement.
• Logistics : Logistics companies ensure faster delivery of your order so, these companies use the data science to find best route to deliver the order.
• Data Scientist : The data scientist develops model like econometric and statistical for various problems like projection, classification, clustering, pattern analysis.
• Data Architect : The Data Scientist performs a important role in the improving of innovative strategies to understand the business’s consumer trends and management as well as ways to solve business problems, for instance, the optimization of product fulfilment and entire profit.
• Data Analytics : The data scientist supports the construction of the base of futuristic and various planned and continuing data analytics projects.
• Machine Learning Engineer : They built data funnels and deliver solutions for complex software.
• Data Engineer : Data engineers process the real-time gathered data or stored data and create and maintain data pipelines that create interconnected ecosystem within an company.

Data science is an interconnected field that involves the use of statistical and computational methods to extract insightful information and knowledge from data. Data Science is simply the application of specific principles and analytic techniques to extract information from data used in planning, strategic , decision making, etc.

Data Science Data Analytics Data Science is used in asking problems, modelling algorithms, building statistical models. Data Analytics use data to extract meaningful insights and solves problem. Machine Learning, Java, Hadoop Python, software development etc., are the tools of Data Science. Data analytics tools include data modelling, data mining, database management and data analysis. Data Science discovers new Questions. Use the existing information to reveal the actionable data. This domain uses algorithms and models to extract knowledge from unstructured data. Check data from the given information using a specialised system.

Python is easy to learn and most worldwide used programming language. Simplicity and versatility is the key feature of Python. There is R programming is also present for data science but due to simplicity and versatility of python, recommended language is python for Data Science.

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Applied Plotting, Charting & Data Representation in Python

This course is part of Applied Data Science with Python Specialization

Financial aid available

191,391 already enrolled

(6,243 reviews)

What you'll learn

Describe what makes a good or bad visualization

Understand best practices for creating basic charts

Identify the functions that are best for particular problems

Create a visualization using matplotlb

Skills you'll gain

• Python Programming
• Data Virtualization
• Data Visualization

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There are 4 modules in this course

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data.

This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

Module 1: Principles of Information Visualization

In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module.

What's included

8 videos 6 readings 1 peer review 1 app item 1 discussion prompt

8 videos • Total 38 minutes

• Introduction • 4 minutes • Preview module
• Updates • 1 minute
• About the Professor: Christopher Brooks • 1 minute
• Tools for Thinking about Design (Alberto Cairo) • 8 minutes
• Graphical heuristics: Data-ink ratio (Edward Tufte) • 4 minutes
• Graphical heuristics: Chart junk (Edward Tufte) • 5 minutes
• Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte) • 3 minutes
• The Truthful Art (Alberto Cairo) • 8 minutes

6 readings • Total 80 minutes

• Syllabus • 10 minutes
• Help us learn more about you! • 10 minutes
• Notice for Coursera Learners: Assignment Submission • 10 minutes
• Dark Horse Analytics (Optional) • 10 minutes
• Useful Junk?: The Effects of Visual Embellishment on Comprehension and Memorability of Charts • 30 minutes
• Graphics Lies, Misleading Visuals • 10 minutes

1 peer review • Total 60 minutes

• Graphics Lies, Misleading Visuals • 60 minutes

1 app item • Total 30 minutes

• Hands-on Visualization Wheel • 30 minutes

1 discussion prompt • Total 10 minutes

• Must a visual be enlightening? • 10 minutes

Module 2: Basic Charting

In this module, you will delve into basic charting. For this week’s assignment, you will work with real world CSV weather data. You will manipulate the data to display the minimum and maximum temperature for a range of dates and demonstrate that you know how to create a line graph using matplotlib. Additionally, you will demonstrate the procedure of composite charts, by overlaying a scatter plot of record breaking data for a given year.

7 videos 2 readings 1 peer review 2 ungraded labs

7 videos • Total 60 minutes

• Introduction • 1 minute • Preview module
• Matplotlib Architecture • 6 minutes
• Basic Plotting with Matplotlib • 10 minutes
• Scatterplots • 12 minutes
• Line Plots • 12 minutes
• Bar Charts • 7 minutes
• Dejunkifying a Plot • 8 minutes

2 readings • Total 60 minutes

• Matplotlib • 30 minutes
• Ten Simple Rules for Better Figures • 30 minutes

1 peer review • Total 180 minutes

• Plotting Weather Patterns • 180 minutes

2 ungraded labs • Total 120 minutes

• Module 2 Jupyter Notebooks • 60 minutes
• Plotting Weather Patterns • 60 minutes

Module 3: Charting Fundamentals

In this module you will explore charting fundamentals. For this week’s assignment you will work to implement a new visualization technique based on academic research. This assignment is flexible and you can address it using a variety of difficulties - from an easy static image to an interactive chart where users can set ranges of values to be used.

6 videos 3 readings 2 peer reviews 3 ungraded labs

6 videos • Total 65 minutes

• Subplots • 15 minutes • Preview module
• Histograms • 12 minutes
• Box Plots • 10 minutes
• Heatmaps • 8 minutes
• Animation • 7 minutes
• Widget Demonstration • 10 minutes

3 readings • Total 50 minutes

• Selecting the Number of Bins in a Histogram: A Decision Theoretic Approach (Optional) • 10 minutes
• Assignment Reading • 30 minutes
• Understanding Error Bars • 10 minutes

2 peer reviews • Total 240 minutes

• Practice Assignment: Understanding Distributions Through Sampling • 120 minutes
• Building a Custom Visualization • 120 minutes

3 ungraded labs • Total 180 minutes

• Module 3 Jupyter Notebooks • 60 minutes
• Practice Assignment: Understanding Distributions Through Sampling • 60 minutes
• Building a Custom Visualization • 60 minutes

Module 4: Applied Visualizations

In this module, then everything starts to come together. Your final assignment is entitled “Becoming a Data Scientist.” This assignment requires that you identify at least two publicly accessible datasets from the same region that are consistent across a meaningful dimension. You will state a research question that can be answered using these data sets and then create a visual using matplotlib that addresses your stated research question. You will then be asked to justify how your visual addresses your research question.

4 videos 3 readings 1 peer review 2 ungraded labs

4 videos • Total 31 minutes

• Plotting with Pandas • 7 minutes • Preview module
• Seaborn • 8 minutes
• Mapping and Geographic Investigation • 12 minutes
• Becoming an Independent Data Scientist • 1 minute

3 readings • Total 23 minutes

• Spurious Correlations • 10 minutes
• Post-course Survey • 10 minutes
• 5 reasons to keep going • 3 minutes

1 peer review • Total 120 minutes

• Becoming an Independent Data Scientist • 120 minutes
• Module 4 Jupyter Notebooks • 60 minutes
• Project Description • 60 minutes

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The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.

Recommended if you're interested in Data Analysis

University of Michigan

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Taking this course by University of Michigan may provide you with a preview of the topics, materials and instructors in a related degree program which can help you decide if the topic or university is right for you.

Master of Applied Data Science

Degree · 1 – 3 years

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6,243 reviews

Reviewed on Feb 13, 2019

Inspires you to create attractive visualisations with a balanced representation, while creating something what you really want, while actively suggesting to explore the API to get to that result.

Reviewed on Mar 6, 2018

Very helpful to understand what it takes to make a scientific and sensible visual. Recommended for someone who is interested in learning data visualization and does not have a background.

Reviewed on Jan 12, 2022

Beautifully designed course to grasp and utilize the knowledge gained. Also the assignments are meant to utilize real world data and practical solutions to it! wonder course, highly recommended!

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