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Entry Level Data Analyst Resume Examples: Proven To Get You Hired In 2024

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Entry Level Data Analyst Resume Template

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As an entry-level data analyst, you'll be diving into the world of data-driven insights and decision-making. With companies increasingly relying on data for growth and improvement, this role is vital to their success. When crafting your resume, it's essential to demonstrate both your technical skills in data analysis and your understanding of the business context. Keep in mind, employers are looking for candidates with a strong foundation in data manipulation and visualization who can also bring unique insights to the table. In recent years, there's been a shift towards using more advanced tools and programming languages for data analysis, like Python and R. So, ensure your resume highlights your proficiency in these areas, as well as your experience working with databases, data visualization tools, and analytical software. Showcasing your ability to adapt to industry trends will make you stand out among other applicants.

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Recruiter Insight: Why this resume works in 2022

Tips to help you write your entry level data analyst resume in 2024,    highlight relevant coursework and projects.

As an entry-level candidate, you might not have extensive work experience in data analysis yet. To showcase your skills, focus on relevant coursework, academic projects, or internships that included data analysis tasks. Include specific examples of how you've applied analytical techniques to solve problems or discover insights.

   Demonstrate proficiency in programming languages

Employers often seek data analysts with programming skills in Python, R, or SQL. Make sure to list these languages and any other relevant tools (like Tableau or Power BI) in a "Technical Skills" section of your resume. If possible, include examples of projects that required using these languages to analyze and visualize data effectively.

If you're a recent graduate or student, use this entry-level data analyst resume template when applying to jobs. It uses extra-curricular and project sections to supplement your work experience.

   University projects

If you are applying for an entry level data analyst job and don't have too much work experience, don't worry! Use data analyst projects like in this resume example to showcase skills like creating predictive models.

   Strong action verbs

Resumes need to use strong action verbs , which immediately tell a recruiter your role in a specific accomplishment. Data analyst resumes should use action verbs that are relevant to data analysis, processing and visualization. Action verbs like "Analyzed", "Assessed" or "Researched" are strong action verbs that effectively showcase data analyst skill sets.

Business Data Analyst Resume Sample

Data science analyst resume sample, data reporting analyst resume sample, data quality analyst resume sample.

We spoke with hiring managers at top companies like Amazon, Google, and Microsoft to gather their best advice for writing an entry-level data analyst resume. They've seen thousands of resumes and know exactly what they look for in top candidates. Here are their insider tips for creating a resume that will get you hired.

   Highlight your SQL and data analysis skills

As an entry-level data analyst, it's crucial to showcase your technical skills, especially your proficiency in SQL and data analysis tools. Hiring managers want to see specific examples of how you've used these skills to solve problems and drive results.

Here are some ways to highlight your SQL and data analysis skills:

• Analyzed a dataset of 50,000 customer records using SQL queries to identify trends and insights, resulting in a 15% increase in customer retention
• Created dashboards and visualizations in Tableau to track key metrics like sales revenue and website traffic, leading to data-driven decisions that boosted revenue by 20%
• Collaborated with cross-functional teams to define KPIs and build SQL queries to track progress, resulting in a 25% improvement in marketing campaign performance

   Quantify your impact with metrics and results

When describing your experience and projects, always try to quantify your impact with specific metrics and results. This helps hiring managers understand the scale and significance of your contributions.

Compare these two examples:

• Analyzed sales data to identify trends and insights
• Analyzed sales data using SQL and Python to identify trends, resulting in a 10% boost in revenue and a 20% reduction in customer churn

The second example is much stronger because it includes specific metrics that demonstrate the impact of the analysis.

   Tailor your resume to the job description

Hiring managers want to see that you have the specific skills and experience they're looking for. One of the best ways to do this is to tailor your resume to the job description.

Here's how:

• Carefully review the job posting and identify the key skills and requirements
• Make sure your resume highlights your relevant skills and experience that match the job description
• Use similar language and keywords from the job posting in your resume where relevant

For example, if the job description emphasizes SQL skills and experience with Tableau, make sure your resume includes specific examples of your SQL and Tableau projects.

   Include relevant projects and coursework

As an entry-level candidate, you may not have a lot of professional experience yet. That's okay! You can still showcase your skills and potential by including relevant projects and coursework on your resume.

Here are some examples:

• Completed a capstone project analyzing a dataset of 100,000 Yelp reviews using Python and SQL to build a recommendation engine, resulting in a 30% improvement in user engagement
• Collaborated with a team of 4 to analyze sales data for a class project, using R to build predictive models that forecasted revenue with 95% accuracy

When including projects, focus on the skills you used, the scale of the data, and the impact of your analysis.

   Emphasize your communication and collaboration skills

Data analysts don't work in a vacuum - they need to be able to communicate their findings and collaborate with cross-functional teams. That's why hiring managers look for candidates with strong communication and collaboration skills.

To emphasize these skills on your resume, try including:

• Specific examples of how you've communicated complex data insights to non-technical stakeholders
• Projects where you collaborated with cross-functional teams to solve business problems
• Presentations or reports you created to share your findings with others

Collaborated with marketing and sales teams to analyze customer data and develop targeted campaigns, resulting in a 15% increase in conversions and a 20% boost in revenue

   Use a clean and easy-to-read format

Finally, make sure your resume is easy to read and visually appealing. Hiring managers often have to review hundreds of resumes, so a clean and well-organized format can help yours stand out.

Here are some tips:

• Use a clear and legible font like Arial or Calibri
• Keep the font size between 10-12 points
• Use bullet points to break up text and make it easier to scan
• Keep the layout simple and consistent, with clear section headings
• Save your resume as a PDF to ensure the formatting stays consistent across devices

By following these formatting tips, you'll make it easier for hiring managers to quickly review your resume and identify your relevant skills and experience.

Writing Your Entry Level Data Analyst Resume: Section By Section

  summary.

A resume summary, also known as a professional summary or summary statement, is an optional section that appears at the top of your resume. It provides a brief overview of your professional experience, skills, and achievements relevant to the job you're applying for. While a summary is not mandatory, it can be beneficial for entry-level data analysts to provide context and highlight their most relevant qualifications.

It's important to note that you should never use an objective statement in place of a summary. Objective statements are outdated and focus on what you want from the employer, rather than what you can offer them. Instead, use your summary to showcase your value and fit for the specific data analyst role.

To learn how to write an effective resume summary for your Entry Level Data Analyst resume, or figure out if you need one, please read Entry Level Data Analyst Resume Summary Examples , or Entry Level Data Analyst Resume Objective Examples .

1. Tailor your summary to the data analyst job description

When crafting your summary for an entry-level data analyst position, it's crucial to align your skills and experiences with the requirements outlined in the job description. This demonstrates to the hiring manager that you understand the role and possess the necessary qualifications.

Here's an example of a tailored summary:

Recent graduate with a Bachelor's degree in Statistics and a strong foundation in data analysis, data visualization, and programming languages such as SQL and Python. Seeking to leverage my skills in data cleaning, statistical analysis, and problem-solving to contribute to data-driven decision-making in an entry-level data analyst role.

Avoid using a generic summary that could apply to any job, like this:

Hardworking and motivated individual with a passion for learning and growth. Possesses strong communication and teamwork skills. Seeking an opportunity to apply my knowledge and skills in a challenging role.

2. Highlight relevant technical skills and tools

As an entry-level data analyst, it's essential to showcase your technical skills and proficiency in relevant tools and technologies. Hiring managers look for candidates who possess the necessary skills to perform the job effectively.

Include mentions of specific skills and tools, such as:

• Data analysis and visualization tools (e.g., Excel, Tableau, Power BI)
• Programming languages (e.g., SQL, Python, R)
• Statistical analysis and modeling techniques
• Database management systems (e.g., MySQL, PostgreSQL)

Avoid listing soft skills or generic terms in your summary, as they don't provide concrete evidence of your abilities. For example:

• Strong problem-solving skills
• Excellent communication and teamwork abilities
• Fast learner and adaptable

  Experience

Your work experience section is the heart of your resume. It's where you show hiring managers what you've accomplished and learned in previous roles, and how that translates to the entry level data analyst position you're applying for. Let's break down how to write an effective work experience section step-by-step.

1. Lead with strong data analysis verbs

When describing your previous roles and accomplishments, choose strong verbs that showcase your data analysis skills:

• Analyzed sales data using SQL to identify opportunities for growth
• Visualized marketing campaign performance with Tableau dashboards
• Queried large datasets to answer business questions and drive decisions

Avoid weak, passive verbs that downplay your contributions:

• Helped with data entry and cleaning
• Responsible for updating spreadsheets
• Tasked with running reports

2. Quantify your impact with metrics

Numbers jump off the page and help hiring managers understand the scope and impact of your work. Whenever possible, include specific metrics to quantify your achievements:

• Reduced customer churn 18% by identifying at-risk segments through predictive modeling
• Automated reporting process, saving 10+ hours per week and enabling real-time KPI tracking
• Discovered $50K in cost savings opportunities by analyzing procurement spend data

If you don't have access to exact metrics, estimates are better than nothing:

Analyzed 500+ customer support tickets to surface common issues and recommend solutions, reducing ticket volume by roughly 25%

3. Showcase your technical skills and tools

Entry level data analyst roles look for hands-on skills with tools like SQL, Excel, Tableau, and Python. Weave in the specific tools you used to accomplish your goals:

• Built automated data pipelines with Python to feed Salesforce data into SQL database
• Created Excel macros to automate lead scoring, saving sales reps 5 hours per week
• Designed interactive Tableau dashboard to track marketing spend vs. lead generation

Avoid simply listing tools without context:

• SQL, Python, Excel, Tableau
• Technical skills include data visualization, statistical analysis, and reporting

4. Highlight promotions and increasing responsibility

Show hiring managers your career progression and how you've taken on more responsibility over time. If you earned a promotion, make it clear:

Business Analyst II, XYZ Company (2019-Present) Business Analyst I, XYZ Company (2017-2019)

You can also show increasing responsibility within a single role:

• Completed data analysis coursework and earned SQL certification to take on more complex data modeling projects
• Began contributing to data governance initiatives and documenting ETL processes
• Mentored 2 junior analysts and led lunch-and-learn sessions on data visualization best practices

  Education

Your education section is a key part of your entry level data analyst resume. It's where you show hiring managers that you have the knowledge and skills to do the job well. Here are some tips to make your education section stand out.

1. Put your education section near the top

If you're a recent graduate or have less than 2 years of work experience, put your education section above your work experience. This highlights your most relevant qualification first.

For example:

EDUCATION Bachelor of Science in Data Science, XYZ University, 2022 Relevant Coursework: Data Structures, Algorithms, Statistical Methods, Machine Learning

2. Include relevant coursework and projects

Listing relevant courses and projects shows you have hands-on experience with key data analysis skills. This is especially helpful for entry level candidates.

Some examples:

• Coursework: Data Mining, Big Data Analytics, Business Intelligence
• Senior Capstone Project: Built predictive models to forecast sales, resulting in 10% revenue increase

Avoid listing basic or irrelevant courses like this:

• Coursework: Intro to Computers, English 101, Art History

3. Add data analytics certifications

Certifications show your expertise in specific data analysis tools and methods. If you have room, include relevant certifications in your education section like this:

EDUCATION B.S. in Mathematics, ABC College Certifications: Google Data Analytics Professional Certificate IBM Data Science Professional Certificate

Avoid listing non-relevant or minor certifications that don't add value, like:

• Online Typing Course
• Basic Computer Skills Certificate

4. Keep it short for senior-level roles

If you're a senior-level data analyst, your education section should be brief. Hiring managers will focus more on your work accomplishments.

Good example:

M.S. in Statistics, University of ABC B.S. in Mathematics, XYZ University

Avoid providing too many details:

Master of Science in Statistics, University of ABC, 2005-2007 Bachelor of Science in Mathematics, XYZ University, 2001-2005 Relevant Coursework: Calculus, Linear Algebra, Probability Theory...

  Skills

The skills section of your entry level data analyst resume is crucial. It's where you highlight the technical skills and tools that make you qualified for the job. Hiring managers and applicant tracking systems (ATS) quickly scan this section to assess if you meet the basic requirements.

Here are some key tips to keep in mind as you write your skills section:

1. List your top data analysis skills

Focus on listing your strongest and most relevant data analysis skills in this section. Some key skills to consider including are:

• Statistical analysis : hypothesis testing, regression, clustering
• Data visualization : Tableau, PowerBI, Matplotlib
• Programming : Python, R, SQL
• Databases : MySQL, PostgreSQL, MongoDB

Avoid listing generic skills like 'Microsoft Office' or outdated technologies. Stick to current, in-demand skills that relate directly to data analyst roles.

2. Tailor skills to the job description

Many companies use applicant tracking systems (ATS) to filter resumes based on whether they contain the right keywords. To get past these filters, make sure to include relevant skills from the job description.

Data analysis Visualization Coding

The skills list above is too vague and likely won't align well with ATS filters. Instead, get more specific:

Data analysis: cohort analysis, statistical modeling, predictive analytics Data visualization: building dashboards in Tableau Coding: Python (pandas, numpy), R, SQL

3. Categorize skills into subsections

If you have a variety of technical skills, consider breaking them down into categories. This makes your skills section easier to read at a glance. For example:

Languages : Python, R, SQL Machine Learning : regression, decision trees, natural language processing Big Data : Hadoop, Spark, Hive Data Visualization : Tableau, matplotlib, ggplot2

Grouping related skills together paints a clearer picture of your specialties and expertise.

4. Quantify your proficiency levels

For skills where you have substantial expertise, consider noting your proficiency level. You can use labels like:

Python (Expert) Data analysis in R (Advanced) Tableau (Proficient) AWS (Familiar)

This gives hiring managers a clearer sense of where you excel. But avoid overstating your skills - if you label everything as 'Expert' it may raise eyebrows.

Skills For Entry Level Data Analyst Resumes

Here are examples of popular skills from Entry Level Data Analyst job descriptions that you can include on your resume.

• R (Programming Language)
• Data Analytics
• SAS Programming
• Clinical Research
• Clinical Trials
• Microsoft SQL Server
• Healthcare Information Technology (HIT)
• Electronic Data Capture (EDC)

Skills Word Cloud For Entry Level Data Analyst Resumes

This word cloud highlights the important keywords that appear on Entry Level Data Analyst job descriptions and resumes. The bigger the word, the more frequently it appears on job postings, and the more likely you should include it in your resume.

How to use these skills?

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Resume Guide: Detailed Insights From Recruiters

• Data Analyst Resume Guide & Examples for 2022

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Entry Level Data Analyst Resumes

• Template #1: Entry Level Data Analyst
• Template #2: Entry Level Data Analyst
• Template #3: Entry Level Data Analyst
• Template #4: Business Data Analyst
• Template #5: Data Science Analyst
• Template #6: Data Reporting Analyst
• Template #7: Data Quality Analyst
• Skills for Entry Level Data Analyst Resumes
• Free Entry Level Data Analyst Resume Review
• Other Data & Analytics Resumes
• Entry Level Data Analyst Interview Guide
• Entry Level Data Analyst Sample Cover Letters
• Alternative Careers to a Data Analyst
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• • Analyzed large datasets using SQL and Python, identifying key trends and patterns, which improved data-driven decision-making by 30%.
• • Developed and presented data visualizations using Tableau, resulting in enhanced stakeholder understanding of complex data sets.
• • Collaborated on a project to streamline data collection processes, reducing data processing time by 25%.
• • Assisted in the development of an automated reporting system using Python, which increased report generation efficiency by 40%.
• • Conducted in-depth data analysis to support business strategy development, contributing to a 15% increase in operational efficiency.
• • Participated in a team project to enhance data quality, achieving a 20% improvement in data accuracy.

5 Entry-Level Data Analyst Resume Examples & Guide for 2024

In an entry-level Data Analyst position, you will analyze data sets to help inform business decisions and improve processes. On your resume, highlight relevant coursework, internships, and any projects that demonstrate your analytical abilities. Incorporate technical competencies such as proficiency in Excel, SQL, and data visualization tools like Tableau or Power BI. Additionally, emphasize your problem-solving mindset and ability to communicate insights clearly, as these traits significantly enhance your contribution to a team.

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Entry-Level Data Analyst Resume Example

Resume Format

Resume Experience

Hard & Soft Skills

Certifications & Education

Resume Summary Tips

Additional Resume Sections

Key Takeaways

All data tells a story. Data analysts search for this hidden narrative to effectively share it with others. Creating an entry-level data analyst resume  is a very similar endeavor. Its purpose is to present data and facts, including education, work experiences, skills, and certifications, ensuring the narrative illustrates your suitability for a particular entry-level data analyst position.

Whether you're a recent graduate or starting a new career path, you can draw upon numerous practical and transferable skills to construct this narrative. This resume writing guide, including excellent examples and pro tips, will help you highlight these skills and overcome some of the biggest challenges to writing an entry-level data analyst resume, such as:

• Targeting your resume to particular jobs
• Following best practices to ensure formatting is ATS-optimized.
• Creating a thorough skills section reflecting both hard and soft skills.
• Showcasing your qualifications to demonstrate your potential to succeed without prior data analyst experience.

For even more examples from the data analysis field, check out these additional resources:

• Data analyst entry-level cover letter
• Junior data analyst resume
• Data analyst intern resume
• Data analyst resume

Entry-level  data analyst  resume example

Here's what this applicant does well in their resume:

• Specific achievements: The applicant effectively highlights specific, quantifiable achievements, such as improving data-driven decision-making by 30% at IBM and increasing report generation efficiency by 40% at Accenture, demonstrating their impact in previous roles.
• Relevant skills and certifications: Skills like SQL, Python, and data visualization are emphasized and correspond with relevant certifications such as Advanced SQL for Data Analysis and Tableau training. This alignment showcases the applicant's expertise in the field of data analysis.
• Experience diversity: The applicant's experience ranges from internships in data analysis and business intelligence to volunteering in data analysis at a food bank. This illustrates their soft skills, such as adaptability and versatility when employing their broad skill set.

How to format an entry-level  data analyst  resume

Competition for entry-level data analyst jobs can be fierce. A great way to ensure your resume immediately grabs the attention of employers is through its formatting.

Resume formatting demonstrates skills such as attention to detail, effective communication of data, and creative data visualization. This presents an opportunity to make a positive first impression on employers.

Include these essential elements when formatting your entry-level data analyst resume:

• Creative Elements: Include creative elements , like colors, unique fonts, and decorative designs, to make your resume unique and memorable.
• Header Content: A prominent header  featuring your name, desired job title, and contact information  ensures they stand out to employers at a glance.
• Professional Links: Provide links to your professional portfolio, website, or social media accounts , such as LinkedIn, as resources for further information.
• Ordering: List all entries in reverse chronological order  to ensure your most recent and relevant appear first.
• Proofreading: Impeccable spelling, grammar, and punctuation are vital to demonstrate attention to detail and communication skills.  

Understanding ATS (applicant tracking systems) is another critical element to ensure your entry-level data analyst resume gets noticed. ATS analyzes text on resumes for keywords or phrases corresponding to desired skills and qualifications for a specific job. Employers use these systems to quickly sort resumes and prioritize candidates with qualifications most closely aligned to the available position.

Review these best practices for ATS-optimized formatting on your resume:

• Fonts: Select ATS-friendly fonts matching the overall style of your resume. Some ATS-friendly fonts are Calibri, Arial, and Times New Roman. However, these commonly used fonts can be dull or generic. For more creative font options, try Rubik, Lato, Montserrat, Raleway, Exo 2, and Volkhov, which are all available on the Enhancv Resume Builder.
• Document Type: Doc or PDF files  are typical for resumes and most easily analyzed by ATS. We prefer PDFs, as they maintain your formatting across different operation systems.
• Creative Elements in Text: Avoid using nonessential stylistic features within text. Elements such as emojis and non-standard date formats can impede ATS’ ability to recognize information in these sections.
• Headings and Subheadings: Ensure resume sections are organized and include simple, precise headings. Standard section titles ensure ATS accurately recognizes and extracts information from these sections.

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To improve the organization and ATS optimization of your resume, include the following top-recommended sections for entry-level data analysts:

The top sections on an entry-level data analyst resume:

• Objective statement: Precisely communicates your career goals aligning with the data analyst role and how you can add value, making you stand out to the recruiter.
• Education: The data analyst role typically requires a degree in a quantitative field, such as statistics, mathematics, or computer science; hence, showcasing your relevant qualifications is essential.
• Skills: Draw attention to your tool proficiency, analytical thinking, problem-solving abilities, and technical skills pertinent to the data analysis field.
• Work Experience: Showcases your practical experience in data analysis, projects you've handled, industries you've worked in, and achievements, to establish your fit for the role.
• Certifications: Displaying your initiative to get certified in relevant courses like data science or a particular software like Python or R programming can distinguish you from other candidates.

Despite the variation in desired data analyst qualifications among industries, there are some common, highly sought-after qualifications recruiters look for on entry-level resumes:

What recruiters want to see on your resume:

• Proficiency in data analysis tools: Recruiters look for your familiarity with tools like SQL, Python, R, and Excel, as these are crucial for cleaning, processing, and interpreting data in this role.
• Statistical and mathematical skills: Knowledge of statistics and mathematics is vital in data analysis to confidently interpret and manipulate data, something recruiters prioritize.
• Problem-solving skills: Analysts often encounter issues that need innovative solutions; showing your problem-solving skills can convince recruiters of your ability to handle similar situations.
• Data visualization skills: An ability to translate complex results into understandable visual representations is a sought-after skill, as it aids in better decision-making.
• Degree in relevant field: Having a degree in fields like statistics, mathematics, or computer science shows recruiters that you have a formal education in dealing with complex data, which can be a competitive advantage.

How to write your entry-level  data analyst  resume experience

The goal of an experience section  isn’t just to share your employment background in the data analysis field. It’s an opportunity to evidence transferable skills and highlight achievements in these roles.

Employers use this section to determine how closely past experiences and career progression align with the responsibilities of an available job.

Despite the limited experience novice data analysts have when applying for an entry-level position, a targeted experience section  will ensure employers recognize relevant experiences at a glance. To target your experience section, focus on communicating specific skills with data analysis tools and noteworthy achievements in your descriptions.

Incorporating specific details into experience descriptions better demonstrates relevance. Take a look at how the following non-data analyst experience example becomes relevant by improving the description:

• • Taught data-related curriculum, including statistical analysis and data interpretation.
• • Implemented data-driven teaching strategies and introduced real-world applications of mathematical concepts to improve student performance.
• • Used data visualization software to stay current with industry tools.
• • Participated in a curriculum redesign initiative incorporating data-driven methodologies to increase student engagement.

This applicant’s background, teaching math, certainly required skills relevant to data analysis. However, this experience description fails to include specifics such as quantitative evidence and analysis tools used to clarify their depth of knowledge and successful application of those skills.

• • Designed and implemented data-related projects within the curriculum, exposing students to real-world applications of mathematical concepts, including statistical analysis and data interpretation.
• • Achieved a 20% improvement in student performance by implementing data-driven teaching strategies and introducing real-world applications of mathematical concepts.
• • Integrated data visualization software, including Microsoft PowerBI and Google Data Studio, into teaching, fostering a technologically enhanced learning environment and staying current with industry tools.
• • Led a curriculum redesign initiative incorporating data-driven methodologies, resulting in a 20% increase in student engagement as measured by participation rates and in-class assessments.

This example utilizes specific examples of tools, naming Microsoft PowerBI and Google Data Studio, to demonstrate familiarity with the software. Additionally, they incorporate quantitative evidence like “implementing data-driven teaching strategies” leading to a “20% improvement in student performance”. These elements illustrate exactly how their non-data analyst experience demonstrates readily transferable skills.

To quickly and easily create a targeted data analysis resume for individual applications, follow these steps:  

• Create a resume template including a complete list of your experiences, certifications, education, and skills.
• Make a copy of the template for each new application.
• Review the job description for each new job you apply to, paying close attention to ideal candidate descriptions.
• In the copy, delete all examples that don't align with the job description.
• Edit remaining examples to ensure they include keywords  copied directly from the job description for increased ATS optimization.

How to quantify impact on your resume

As a data analyst, you know numbers and statistics  speak louder than words. Therefore, demonstrating efficiency and impact with quantitative evidence in your experiences section should be a breeze.

It's essential to be mindful when including quantitative data since an overly data-heavy resume risks losing both the impact of the data and the reader's attention. Tailoring your resume by limiting quantifying data to only those supporting skills well-aligned to individual job opportunities is essential.

Here are a few outstanding ideas to incorporate quantitative evidence in your experience descriptions:

• The number of projects you've completed showcases extensive experience handling data analysis tasks.
• Percentages indicating process improvements or efficiency gains you've achieved through your analyses exhibit problem-solving skills and impact efficiency.
• The size of the data sets you've worked with verifies your capacity to manage and analyze big data.
• Quantifiable results and correct predictions from past roles illustrate your accuracy.
• The number of tools or software you are proficient in for tasks such as data mining, data analysis, and data visualization highlights your technical proficiency.
• How many team-based projects you've participated in proves your ability to collaborate professionally.
• The number of spoken languages you are proficient in can showcase your communication skillset, diversity, and global readiness.

When giving statistics, name specific data analysis tools or methodologies used to acquire the data to evidence your capabilities.

How do I write an entry-level  data analyst  resume with no experience

Entry-level data analyst positions assume applicants will have limited direct work experience. Therefore, they present an excellent opportunity for data analysts to gain skills and expertise in the field. Without work experience, however, there are a few additional elements to consider.

The key to writing this section without data analyst employment  is to focus on alternative experiences highlighting the same skills and achievements. These could include experience in school, internships, freelance work, or personal projects.

To communicate qualifications and achievements without prior data analyst employment, follow these steps:

• Compile a list of keywords from the job description.
• Brainstorm previous experiences showcasing the same keyword skills and knowledge from your list. These could be experiences from school, internships, freelancing, or working on personal projects.
• Provide work samples in a projects or portfolio section whenever possible.
• To add credibility, include tangible evidence of transferable skills, such as relevant education and certifications.
• Include quantifiable data to demonstrate your efficacy.
• Avoid including  irrelevant work experiences  or outdated experiences .
• Always list experiences in reverse chronological order to ensure your most recent and relevant examples appear first.

It's common for entry-level applicants to feel their experience section isn't strong enough. Rather than attempting to include irrelevant work examples to incorporate relevant achievements, consider incorporating additional sections  better suited to sharing the qualifications, such as an awards, projects, or publications section.

How to list your hard and soft skills on your resume

Data analysis requires a multifaceted skillset. Your resume’s skills section  serves as a quick reference to highlight traits and abilities that indicate your suitability for a position.

It may be tempting to include a comprehensive list incorporating all your skills. However, creating a targeted list of skills and referencing skills from the job description is more effective.

Follow these steps to write your targeted skills section:

• Format your skills section as a simple list without elaboration or examples.
• Include hard and soft skills, differentiating between them by listing them separately.
• Organize long skill lists into subsections such as “Data Analysis Skills” and “Data Visualization Skills.”
• Target your list for individual positions by including keyword skills from the job description.
• Write your keyword skills precisely as they appear in the job description to enhance ATS optimization.
• Never lie about or exaggerate your skills.
• Keep your experience and skills sections consistent. List the skills mentioned in the experience section on your skills list and demonstrate how you’ve developed skills in your experience descriptions.

Presenting relevant hard and soft skills on your resume gives employers a more holistic idea of who you are as an employee and what data analysis skills you will contribute.

Hard skills

Technical skills  that require study, training, and practice are known as hard skills . In data analysis, these are skills necessary to perform tasks such as data cleaning, statistical analysis, and data visualization.

When considering which hard skills to include on your resume, consult this list of the top hard skills for entry-level data analysts:

Best hard skills for your entry-level data analyst resume

• SQL (Structured Query Language)
• R Programming
• SAS (Statistical Analysis System)
• Database Management
• Data Modeling
• Machine Learning
• Predictive Modeling
• Apache Spark
• Data Visualization
• GIS (Geographic Information Systems)
• Data Cleaning
• SPSS (Statistical Package for the Social Sciences)
• Big Data Analysis
• Advanced Mathematics

Soft Skills

Soft skills  are an often overlooked but potentially valuable resume asset. They are intrinsic, personal skills that make you uniquely qualified to succeed in a role. For example, attention to detail and effective communication are vital soft skills for data analysts when communicating findings and recommendations to stakeholders.

Soft skills are transferable from one job to another but difficult to teach due to their personal nature. Therefore, these skills provide an incredible opportunity to distinguish yourself from other candidates.

These are some of the top soft skills employers look for in entry-level data analysts:

Best soft skills for your entry-level data analyst resume

• Analytical Thinking
• Communication Skills
• Problem-Solving Abilities
• Critical Thinking
• Attention to Detail
• Organization Skills
• Collaboration
• Fast Learner
• Flexibility
• Time Management
• Decision-Making
• Active Listening
• Ethical Judgement
• Positive Attitude
• Perseverance
• Team Leadership
• Conflict Resolution

How to list your certifications and education on your resume

Degrees and certifications verify the hard skills on your resume. Additionally, they quantify soft skills like dedication to "continued learning" and "adaptability to new technologies."

A bachelor's degree in data analytics or a related field, such as statistics, mathematics, computer science, or information technology, is typically preferred for entry-level data analysts. However, specific industries may favor candidates with a related education background, such as in finance, business, or healthcare.

To include an education section on your social media manager resume, follow these steps:

• For each degree, include the name of the university, its location, the dates attended, and the title of your degree.
• Including your GPA is optional unless the job description specifies a minimum GPA requirement.
• Only include your major and minor  if relevant to the open position.

Here's an example of how your education section should appear on your resume:

Certifications are vital in data analysis, where tools and software are continuously developed and updated. Certifications provide evidence of solid, current foundational knowledge and add value that can set you apart from other candidates with similar educational backgrounds.

Certifications for data analysts typically demonstrate skills in programming languages, database platforms, big data analysis, and data analysis tools.  

To share relevant certifications  on your data analyst resume, follow these steps:

• Include the name of the certification, the certifying institution, and the year obtained.
• Limit certifications to current, unexpired credentials. For example, a certificate in SPSS Clementine would no longer be relevant since it has been replaced by newer tools such as IBM SPSS Modeler and IBM Watson Studio.
• Limit your certifications to only those specifically named or directly related to the required skills mentioned in a job description.

Here are a few of the top certifications for entry-level data analysts:

Best certifications for your entry-level data analyst resume

• Tableau Desktop Specialist
• Cloudera Certified Associate (CCA) Data Analyst
• Data Analyst Nanodegree
• Excel to MySQL: Analytic Techniques for Business Specialization
• Power BI Data Analyst Associate

How to write your entry-level  data analyst  resume summary or objective

Personal Statements , such as a summary or objective, are powerful tools for contextualizing the data and facts on your resume and how they correspond to your career progression and goals.

These statements are especially vital for entry-level positions where your career trajectory may not be well established or evident.

Additionally, these sections allow you to highlight well-aligned and transferable data analysis skills and experiences by briefly clarifying how they demonstrate your suitability for an available job.  

The primary difference between a summary and an objective is that a summary statement  focuses on how past employment experiences align with an available job. In contrast, an objective statement  concentrates on your future career goals to illustrate how they correspond with the position.

An objective section is typically preferable for an entry-level data analyst position since applicants have limited experience in the field.

Follow these best practices to write your entry-level data analyst resume objective :

• Customize the statement to match the specific job you're applying for, emphasizing relevant skills and experiences.
• Describe the kind of position you are seeking.
• Describe relevant transferable or soft skills you hope to use in database development.
• Highlight your motivation to learn and gain experience.
• Start sentences with action verbs to make your summary dynamic and impactful.
• Use keywords directly from the job description.
• Be brief. Keep your objective to 3-5 sentences.

A critical element of an objective statement for an entry-level resume is to focus on skills and knowledge you already possess and will utilize to accomplish goals as opposed to focusing solely on what you hope to gain from the experience.

The following examples demonstrate how a shift in focus can improve an entry-level resume objective.  

This applicant shares very few specifics regarding their own experience and skills. They express what they hope to gain from the position. However, the contributions they hope to make aren't clear. Additionally, they miss opportunities to implement keywords by utilizing strong adjectives in their descriptions.

This applicant utilizes active verbs and key terms from the job description to demonstrate their data analysis and finance background. They incorporate keywords by naming specific programs and aspects of data analysis. Additionally, they utilize strong adjectives and active verbs such as “driven,”  “analytical,” “eager,” and “collaborate.” This allows them to go beyond what they hope to accomplish in this position, incorporating what they will contribute to the team.

Rephrasing specifics from the “Qualifications” or “Ideal Candidate” sections of a job description is a great way to guarantee you mention the skills and qualifications most valued by the employer.

Additional sections for entry-level  data analyst  resumes

Determining what to include  and what to omit  on your entry-level data analyst resume can be challenging where space is limited. Supplementary resume sections  allow you to make the most of the space by showcasing distinct qualifications and sharing work samples to differentiate yourself from other candidates with similar technical skills and experience.

• Specializations : Data Analysts may have experience working in a particular industry, such as finance, marketing, or science. If you have extensive experience or certifications in a specific industry directly corresponding to an available position, a specialization section will highlight your expertise.
• Programming languages : The ability to program in various languages such as SQL, Python, and R is a skill many employers value. However, if you include a spoken languages section , distinguish between spoken and programming languages with clear section headings.
• Awards and honors : Notable academic or professional awards , scholarships, or honors related to data analysis are an effective way to demonstrate the quality of your work. Be sure those you list are relevant, recognizable, and verifiable.
• Online profiles : Including links to your well-maintained, professional online profiles aligned with your resume, such as LinkedIn or GitHub, allows employers further access to information and your work.
• Professional portfolio : A portfolio is an excellent way to evidence your abilities through work samples. It can contain code snippets, visualization samples, and explanations of your thought processes.
• Professional memberships : A professional Associations section highlights your dedication to ongoing improvement and staying current in your field. If you’re interested in adding a professional association to your resume, consider joining one of the top professional associations for data analysts below.

Professional associations for data analysts

• Association for Computing Machinery ( ACM )
• American Statistical Association ( ASA )
• Data Science Association ( DSA )
• Data & Marketing Association ( DMA )
• Digital Analytics Association ( DAA )
• Data Science Central ( DSC )

Incorporating additional sections ensures your resume reflects your top, targeted qualifications. If you decide to include a section not named on this list, be sure they use catchy headings  and are ATS-friendly.

Adding a projects section to your entry-level data analyst resume

Entry-level data analysts often grapple with the task of showcasing their capabilities to demonstrate that their novice status doesn’t imply a lack of relevant knowledge or ability.

A powerful strategy to overcome this obstacle is incorporating tangible examples of significant data analysis projects  into your resume.

A projects section is simply a list of relevant projects with short descriptions and links to online portfolios or repositories like GitHub.

Here’s an excellent example of projects on an entry-level data analyst resume:

Sharing your projects can be especially useful if you have limited work experience since project examples can be from school or personal work, which may not fit well in other resume sections .

Here's how to include a projects section on your resume:

• Create a "Projects" section heading .
• Limit projects to only those that showcase skills targeted to the position.
• Add a short, simple title for each featured project.
• Give a brief 1-2 sentence description of the project, including keywords from the job description.
• Provide a URL or link to repositories such as GitHub or Bitbucket.
• Ensure you have the proper permissions before disclosing information about proprietary or work-related projects and repositories before sharing them.
• Organize Projects in reverse chronological order.

Key takeaways

Although your experience may be limited upon entering the data analysis field, a targeted resume will demonstrate the qualifications and capabilities employers value. To ensure your resume highlights your most relevant skills and expertise, review these key takeaways:  

• ATS-optimized formatting helps ensure your resume reaches employers for review.
• Target your resume skills and experiences by including examples aligned to a specific entry-level data analyst position.
• Hard skills refer to your technical skills, such as those relating to programming languages, data analysis tools, and data visualization skills.
• Quantitative data is an excellent way to evidence impact and achievements in past roles but should be tailored to maintain effectiveness.
• Certifications for data analysts demonstrating skills in programming languages, database platforms, big data analysis, and data analysis tools are an excellent way to distinguish yourself from applicants with similar educational backgrounds.
• Without extensive experience, it is critical to demonstrate data analysis abilities in alternative experiences or by incorporating additional sections.
• An objective statement demonstrates how you will utilize your existing knowledge to achieve future career goals in a position.
• Including soft skills is a great way to set yourself apart from other entry-level applicants.
• A projects section is an excellent way to evidence relevant hard skills and experiences.

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29 Data Analyst Resume Examples for 2024

Data Analyst Resume

• Data Analyst Resumes by Experience
• Data Analyst Resumes by Role
• Writing Your Data Analyst Resume

The number of data analysts is expected to  grow by 25 percent  between 2020 to 2030, coupled with the  increase in pay transparency laws  making this the ideal time to get a data analyst job.

Fun fact: before starting BeamJobs, one of our founders worked as a data analyst for six years. With his guidance, we’ve reviewed many data analyst resumes to figure out what helps data analysts get more interviews.

Building a resume  and  data analyst cover letter  is the hardest part of this process. To inspire you, we chose  29 top data analyst resume samples  for different career stages.

Our data analyst resume examples are proven to help you put your best foot forward to get the job you’ve always wanted in 2024.

or download as PDF

Why this resume works

• Once you know what the employers are looking for, you can include directly applicable keywords and matching language in your work experience bullet points (provided the keywords truly describe you!)
• After you’ve determined the content and matching keywords for your bullet points, add in any quantifiable metrics that can showcase your experience and help prove your merit.

Data Analyst Intern Resume

• If you lack paid work experience in a field, consider it an opportunity to include personal projects on your data analyst intern resume where you demonstrated skills relevant to the position you’re applying to.

Entry-Level Data Analyst Resume

• Unsure how to  make a resume ? No problem! Start by using a  solid resume outline  to help you get a feel for what a resume looks like, then add your experience and skills one at a time.
• The first is to demonstrate programming, testing, modeling, and data visualization competency by building well-designed projects that solve real problems through code.
• The key here isn’t reinventing the wheel but creating something dynamic and unique that can’t be easily replicated with a few Google searches and a video tutorial.
• The second option is to invest time and effort into internships. They’re a fantastic way for an aspiring degree-holder to gain on-the-job experience.
• Some internships require a completed degree before starting. Although this is becoming more uncommon due to online coding trade schools, do some research regarding markets and locations.

Entry-Level Risk Adjustment Data Analyst Resume

• To impress hiring managers, include programming languages you’re familiar with, what you hope to do at your future job, and some of your passions within the field.
• The first is to build well-designed scalable projects that solve real problems through code. So, if you haven’t done any projects, now’s the time to start! Projects are a great way to fill your resume if you don’t have work experience.
• The second option is to get an internship. Some internships require a degree, but online coding trade schools and boot camps are becoming more popular and feasible.

Junior Data Analyst Resume

• Even when a project wasn’t part of a previous job, be as specific as you can by including examples or metrics to show your impact! 
• Ensure that any projects or accomplishments such as internships, classes, or volunteer initiatives, relate directly to the job role. 
• Including a career objective  can also help bulk up your resume with information about what you can offer. 
• Examples and quantifiable metrics of success can make any resume outshine the competition: And they’re extra important for junior data analysts! 
• Since data analysis focuses so heavily on numbers like profits and improvement percentages, include plenty of these to back your credibility. 

Senior Data Analyst Resume

• Highlight a lengthy career in data analyst roles with quantifiable data from multiple sources, jobs, leadership, and mentoring.
• Only include highly relevant ones like Python, SQL, Tableau, and Excel with additional modeling, data visualization, and product analytics keywords.

Senior Insurance Data Analyst Resume

• Including a  resume summary  on your senior insurance data analyst resume helps you highlight your lengthy career with quantifiable data from various sources and can help you land an interview by setting you apart from more novice competition!
• This  resume format  allows the employer to read your work history like an unfolding story, but with the punchline first.
• Only list  skills on your resume  that truly apply to the role at hand; you’ve got limited space—don’t waste it.

Experienced Data Analyst Resume

• The Standout template is basically your best friend since it lets you add your education, skills, and work experience without cramming everything. Cool tones like royal blue further give your experienced data analyst resume the professional yet visually appealing vibe.

HR Data Analyst Resume

• Don’t send your HR data analyst resume out the door without adding hard-hitting numbers like “Formulated 2 advanced Microsoft Excel models… saving $13,941 in extra talent acquisition costs.” This way you’re not just speaking data, you’re displaying its monetary value!

Power BI Data Analyst   Resume

• Take your Power BI data analyst resume up a notch by highlighting any Power BI-based task you’ve led and the kind of impact it created for the company.

Excel Data Analyst   Resume

• Use the Standout template from our library to have enough space for adding all your skills, experience, and education. Include as many skills as you can in your experiences to prove you can always switch from Excel to other tools to extract and refine data.

Python Data Analyst   Resume

• Take a leaf from how William bolds and underlines reducing vehicle downtime by 27% and saving $128K monthly in yearly maintenance costs. Similarly, identifying trends and revenue growth opportunities that increased monthly sales by $101,972 is sure to catch the hiring manager’s eye. You could italicize instead of underlining but let the bold stay.

Data Governance Analyst   Resume

• For your case, achievements that touch on cutting costs (cue:  saving 13% in infrastructure expenses ) and reducing process times (cue:  saving 12 hours per week ) would propel your candidacy to early success.

Data Quality Analyst Resume

• Therefore, to have a soft landing for your application, don’t spare any skills that you feel are relevant to the job—either in your resume or letting an AI cover letter generator have a go at your application. Including your mastery in Talend Data Quality, QuerySurge, Informatica Data Quality, and more would be a great strategy to impress potential employers.

Financial Data Analyst   Resume

• List down the best financial institutions like Citi and Deloitte that you’ve worked for (even for a position as low as an intern or volunteer!) and you’ll be on every employer’s hiring list. It’s also a great idea to list any certifications you hold such as “Chartered Financial Analyst.”

R Data Analyst Resume

• Follow John’s lead and state how you’ve worked with data and specifically (very important!) on R-based frameworks. Keep things simple and vary how you’ve helped each company during your tenure.

Alteryx Data Analyst   Resume

• For instance, rather than simply listing your proficiency in data manipulation, describe how you leveraged your expertise in this area to free up storage space and increase efficiency for the organization.

Big Data Analyst Resume

• Take your big data analyst resume to the next level by demonstrating your prowess in presenting actionable analyses to key organizational stakeholders.

Clinical Data Analyst Resume

• Even a candidate with years of industry experience can optimize their clinical data analyst resume with an additional certification. Make sure recruiters can easily spot that you’re a Certified Clinical Data Analyst (CCDA) to add an extra level of credibility to your background.

Creative Data Analyst Resume

• This will demonstrate to hiring managers that you bring a fresh perspective to the table—always a plus on a creative data analyst resume!

Lead Data Analyst Resume

• Formatting your resume  in reverse-chronological order is always a good way to wow recruiters with your most recent experience and relevant accomplishments. Put your best foot forward!
• Make sure your examples of leading roles are also as well-rounded as possible since this is another quick way to demonstrate how much you’ve learned throughout your career. 
• Since lead data analyst roles are so focused on details and critical thinking, make sure you highlight these areas, too! 
• Use solid, quantifiable metrics like improvement rates and dollar-amount company savings to back up your achievements. 

Healthcare Data Analyst Resume

• For instance, if you’ve completed a B.S. in Health Care Informatics, it means you’ve spent an extensive period of time learning how to analyze and interpret healthcare data and information—which is what most recruiters will be looking for in your healthcare data analyst resume .

Business Data Analyst Resume

• By doing so, you put your most recent accomplishments at the very top of your business data analyst resume —which is where a recruiter or your next potential employer is going to look first.

Marketing Data Analyst Resume

• For instance, if you’ve completed a Professional Certified Marketer (PCM) certification, it’s clear you understand the nuances required to be a marketing professional. Then, you can use the work experience bullet points to focus on the analysis aspect of your role.

AWS Data Analyst Resume

• You only have a couple of sentences to do so, but it’s still a powerful way to tell a recruiter exactly what you bring to the table and what they stand to gain if they hire you.

SQL Data Analyst Resume

• Start by analyzing the requirements in  data analyst job descriptions  to get an idea of what employers require.
• Speak with your current manager if you’re anxious about changing position titles. Always err on the side of caution, and ask for permission instead of forgiveness.

Data Analytics Manager Resume

• Making your resume easy to read  can be as simple as using a  resume template , but it also means  condensing your bullet points as much as you can ,  including metrics  to boost your credibility quickly, and leaving just enough white space to make it a breeze to skim.
• The first is the job description’s list of required skills. The second source is a job board advertising analytics manager roles in a specific geographic location. 
• Both sources give you a general idea of what you need to emphasize in your bullet points and what KPIs to use to complement your experience.

Revenue Reporting Data Analyst Resume

• Your resume should be formulated specifically to target the list of requirements from the company  job description . 
• Use our  resume checker  and a spellcheck extension (like Grammarly) to ensure your resume is error-free.

Data Analyst/Finance Analytics Resume

• Including numbers and percentages is the fastest way to show employers your qualifications. Use metrics to show how you’ve made significant improvements whenever you can.
• Using professional yet stylish  resume templates  and  resume outlines  equips you to add sections specifically for certificates and licenses.
• Don’t be afraid to use a hint of modest color to enhance the overall look of your resume. You’re a pro with personality after all!

Related resume guides

• Data Science Resume
• Financial Analyst
• Computer Science
• Data Engineer

Data Analyst Resume Guide for 2024

According to the  U.S. Bureau of Labor Statistics , the employment of computer and information research scientists (including data analysts) is projected to grow 16 percent from 2018 to 2028. This is much faster than the average for other jobs!

Due to the high demand and high wages, it makes sense that people are flocking to apply for data analyst roles.

But that doesn’t mean you should be discouraged. Applying online to jobs can feel like applying in a black hole, and we know it sucks.  It took one of our founders 77 job applications  before he landed his first data analyst job at Geico, and the next job was much easier to get.

One issue with applying for data analytics roles is that these titles are not standardized across different companies. At one company, data analysts might spend their time building reports in Tableau, while at another, they might be writing machine learning models for production.

Because of this variability, it can be tough to be sure to include the correct information in your data analytics resume. With this guide, you’ll put your best foot forward, no matter which data analyst roles you’re seeking!

Show off your data analyst skills

First, you need to show you have the right abilities for the job! This means you need to accomplish two goals with the  skills section of your data analyst resume . First, you have to be able to get past the automatic keyword filters in the applicant tracking system (ATS), which companies use to filter applicants. Next, you want to demonstrate your technical proficiency to the person reviewing your resume.

If you’re unsure which skills to include, we analyzed the data to learn  which skills are most in demand for companies hiring data analysts .

These two goals are, unfortunately, mostly in opposition to each other. If your goal was to just get past the ATS, you’d list every skill to get your foot in the door:

Bad — avoid a long list of generic skills

The problem? This method is a big red flag to technical hiring managers. You should only include skills you’d be comfortable discussing in your  data analyst interview . Plus, your  skills section shouldn’t take up more than 20 percent  of the page.

Any reasonable employer won’t expect you to know SAS, R,  and  Python. Instead, just list the technical skills that you’ve coded in before. And avoid including a skill like “data mining” or “data analysis” since a technical hiring manager knows these are just blanket terms that don’t mean much. Instead of “data mining,” list actual techniques you’ve used, like “decision trees” or “logistic regressions.”

But even when narrowing it down, sometimes your skills list looks cluttered and hard to read. Never fear; there are multiple ways to organize your skills section! For starters, you can list your skills by how proficient you are with them (“Advanced” vs. “Familiar,” for example). Alternatively, you can list your skills by skill type. You can even mention the years of experience you have with each tool.

For programming languages, mention the libraries and frameworks you use for data visualization and manipulation in that programming language.

Good—specific skills and modeling techniques

Your data analyst objective

Most of the time, you don’t need to include a  resume objective  or  resume summary ; a mistake many data analysts make. If a resume summary or objective doesn’t add value to your application, it’s okay to leave it out.

Here’s a sample data analyst resume objective that you would want to leave off of your resume:

Bad—uninformative resume objective

Why leave this off? It’s redundant. Suppose you already demonstrate in your resume that you used tools like Python and SQL to turn data into actionable insights. In that case, your objective doesn’t tell the person reviewing your resume any new information.

When to include a resume objective

• You’re undergoing a career change. You can say something like, “After 4 years of working in operations, I am now looking to leverage my expertise in turning data into insights as a data analyst.”
• You have a specific interest in a given role or company that makes you an especially strong candidate. For example, “Reading Match.com’s data blog ignited my pursuit of a career in data analytics, and I want to further contribute to the data-driven culture at the company.”

Summaries are similar, but they’re for candidates with over 10 years of experience and can include more personal achievements. There isn’t much difference between a resume summary or an objective; all you need to know is when you should include them.

Data analyst resume format

Here are some quick tips for formatting your data analytics resume:

• Keep it to one page.
• Break up bullet points into small, consumable pieces of information.
• Don’t refer to yourself with “I” or “we,” as it takes up room and looks unprofessional.
• Double, triple, and quadruple-check your grammar and spelling. One error can send your resume into the “no” pile!
• Each bullet point on your resume should be a self-contained, complete thought.

When a hiring manager reviews 50+ resumes for a given role, they quickly look for reasons to say “no.” By using these  resume-formatting tips , you make it easier for the hiring manager to see your worth and ask you for an interview, getting you one step closer to a job. 

Contact information

Of all the places to make an error, your contact information is the  worst  place to have it happen. One of our team members recounted their early days out of college as a data analyst. When they were applying for jobs, they accidentally wrote the wrong email address on their resume for seven different positions.

Even if they were perfectly qualified for the role, there was no way to contact them because of a minor mistake. So believe us when we say you need to  triple-check this section for any spelling, grammar, or link errors .

As part of your contact information, you should include your name and the role you’re applying for (even if it’s not your current role).

You don’t need to include your full address in this section, but you should list your city and zip code. You also need your phone number just in case your employer prefers that method.

Finally, include a link to your LinkedIn profile and anything else that might convey why you’re a great data analyst. If you have an active Github, include a link to that. If you do a lot of Kaggle contests, include a link to your profile. Have a personal blog where you talk about election data? Be sure to include a link.

Data analyst projects for your resume

If you’re entry-level and looking for your first full-time role, including projects on your data analyst resume is an absolute must. However, the more work experience you get, the more projects should become less critical. By the time you have four-plus years of experience in the field, you should only include a project of which you’re exceptionally proud.

What projects should you list? Anything where you identified (or were given) a problem and you used data to come up with an answer to that problem. It’s okay if it’s a class project, but it’s even better if you took the initiative yourself.

If you don’t have any such projects, now is the time to work on some. Do you have a question you’ve never answered? An experiment you’ve been longing to try? Think of a way to gather and analyze data to sate your curiosity.

Here’s an example: one of our founders had a hunch that the major job boards (Indeed, Glassdoor, and LinkedIn) essentially had the same jobs for data science roles. So, he manually collected data, analyzed it, and wrote about it to determine the  best job board for data scientists .

The projects you include don’t need to be exhaustive or ground-breaking. Employers just want to see that you can ask a question, use data to answer it, and present your findings reasonably and clearly.

Good—show you can answer your own questions with data

When talking about your projects, here’s how you should frame what you did:

• Clearly state the question you were answering or the problem you were trying to solve
• Show what tools or languages you used to solve the problem
• State the impact of the work you did

Your projects section is also an opportunity to provide more context around the programming languages and libraries you listed in your “skills” section. 

Like the “projects” section, the education section of your resume will be longer for entry-level data analysts relative to more experienced data analysts. You’ll want to include relevant courses you took in school related to data analytics for entry-level data analysts.

Courses relevant to data analytics are any mathematics, statistics, programming, and economics classes you took. To be an effective data analyst, you need to apply the principles you learned in these classes to real-world problems and datasets.

For entry-level roles, include relevant classes you took in school

Regardless of your experience level, you should always mention the school you attended, what you majored in (including minors or certifications), and when you graduated. This would also be the place to list any boot camps or relevant online courses you may have taken in the field.

If your background is in academia, you can also list any publications you may have co-authored. Be sure to include the title of the magazine and a link to allow the hiring manager to read further if they’re interested.

Only mention your GPA on your resume if it’s something you want to highlight—generally, only list your GPA if you’re entry-level and obtained anything above a 3.0.

Work experience

You analyze data for a living, so you know that numbers count when it comes to information. So when you’re talking about your work experience, your goal should be to highlight your accomplishments using numbers and estimates.

The formula for talking about work experience

“Specific contribution to project mentioning specific tools and skills”

“quantitative impact of the project”

“Performed a customer cohort analysis using SQL and Excel and recommended an email campaign for one customer segment”

“that lifted monthly retention by 10%”

Enter your text here…

When discussing your work, especially if it was a team project, emphasize your specific contributions. For example, you may have made a product recommendation based on a previous analysis. You’d want to talk about that particular recommendation on your resume instead of the built feature.

When talking about the quantitative impact, it’s okay to talk about the project as a whole. Following the example above, it’d be impossible to tease out the value of your product recommendation versus the engineer’s impact who built the feature since it’s a team effort. You’d say the feature had a revenue impact of $X on your resume.

Data analysts work across many different teams and projects in a company, so it’s not always easy to tie your work to a revenue impact. Still, try estimating your contributions using metrics to make your resume stand out.

These can be very rough estimates; you just want to make it clear that you’ve contributed to positive outcomes for the businesses where you worked.

Ways to quantify the impact of your analytics work

• “Used Python and SQL to determine a specific change in the landing page, resulting in a 10% boost in free trial activation rate”
• “Streamlined and automated a key business report in Tableau, saving the team 10 hours of reporting each week”
• “Used SQL and Excel to recommend ending contracts with worst-performing vendors, resulting in a costs savings of $100,000 annually”
• “Built data visualizations in Excel to demonstrate the efficacy of marketing plan, resulting in the close of a $1.3M Series A”
• “Determined through analysis in Python that emailing customers who had been inactive for 7 days resulted in a retention improvement of 7 basis points”
• “Identified procedural areas of improvement in hiring data to improve the time-to-hire for key roles by 11 days”
• “Used SQL and Excel to identify common complaints amongst new customers, leading to changes that improved new customer satisfaction by 14%”

When formatting your work experience, always list your most recent work at the top of your resume and list your other positions in reverse-chronological order.

Just to hammer home our point even further, here’s an example of the same work experience. One is stated in a quantitative impact, and one is not. 

Bad—no quantitative impact

Good—quantitative impact

Tailor your resume for each job

For each role to which you apply,  make minor edits to your resume  based on the  data analyst job description . Fortunately, you don’t have to completely rewrite your resume; just a few tweaks will do.

For example, let’s say you’ve done projects in both Python and R, and your resume heavily leans into your Python experience. If you apply to a job that mentions R, you should change your resume to discuss your R experience.

Similarly, if you have specific projects that relate to the job you’re applying for, include those projects. If you’re applying for a marketing data analyst role and have experience building marketing mix models, your application will become significantly stronger by mentioning those mix models.

Let’s say you’re applying to this job:

This seems like a heavy data visualization role. Instead of mentioning predictive modeling, talk extensively about your experience building robust data visualization in Tableau.

Change this:

How to Write an Effective Data Analyst Resume

Here are the major takeaways you should keep in mind when  writing a professional resume :

• Keep it to one page and  proofread, proofread, proofread .
• Otherwise, don’t let your education section take up a lot of space.
• You don’t need a summary or objective section on your resume unless you’re undergoing a career change or have over 10 years of experience.
• Only include skills on your resume for which you’d be comfortable being interviewed.
• Mention your specific contributions and quantify the overall project’s impact on the business.

By following this guide, you’ll be able to quickly and convincingly make the case that you’re a great fit for the data analyst role for which you’re applying. 

Applying for jobs isn’t easy, but you’ve taken a huge first step toward landing that dream job. Now all that’s left is to write,  double-check your resume  for errors, and submit it to your dream job!