Data Analyst Resume & Portfolio

Our Data Analyst Career Guide Series:

Data Analyst Overview
Data Analyst Skills & Tools
Data Analyst Training & Education
Data Analyst Career Path & Salary
Data Analyst Portfolio & Resume <– You Are Here
Data Analyst Interviewing

Don’t be fooled by the data analyst skills shortage and skyrocketing surge in open roles. Despite 5.5% growth this year and 304% growth projected over the next decade, competition can still be fierce for coveted spots. One hiring manager says he sifted through over 500 applicants for a data analyst job in two weeks’ time. And let’s face it: every worker these days should be able to demonstrate some type of data literacy skills—which means a crowded field of competing candidates.

The secret to getting your foot in the door? Deliver a strong data analyst portfolio and resume. This guide provides tips and templates to help you get started.

Building a Data Analytics Resume

First impressions start in the inbox. A well-crafted resume doesn’t just list your skills and experiences—it tells a compelling story of your capabilities and potential. Here’s how to build a standout data analytics resume that captures attention.

Resume Structure

The best data analyst resumes combine technical expertise with problem-solving and communication skills, showcasing the ability to interpret complex data and derive actionable insights.

Prospective employers will expect the following resume format:

1. Contact Information: At the top of your resume, include your name, phone number, email address, and LinkedIn profile. This is where potential employers will look first to get in touch.

2. Professional Summary: Next, add a brief section (2-3 sentences) summarizing your data analytics expertise. Mention key skills and highlight any major accomplishments that make you a great fit for the role you’re applying for. Alternatively, you might write a sentence about the type of job you’re seeking. Be sure to include some of the language from the job description.

3. Education: List your educational background, including any relevant certifications, including the Data Analytics Bootcamp or Python Programming Short Course if you’ve taken them.

4. Experience: Outline your professional experience in reverse chronological order. Focus on roles where you’ve applied data analytics skills. Include the company name, your job title, and the dates of your employment. Use bullet points to describe your key responsibilities and achievements, emphasizing quantifiable results where possible.

5. Skills: Highlight proficiency in SQL, Excel, or Python. Mention any relevant techniques or methodologies you are familiar with, such as statistical analysis or machine learning.

6. Projects: Showcase projects that demonstrate your data analytics abilities. Include a brief description of each project, the tools and techniques used, and the outcomes. This could be coursework, personal projects, or contributions to open-source initiatives.

7. Certifications: If you have any certifications related to data analytics, such as the General Assembly Data Analytics Short Course, include them in this section.

8. Additional Sections: Depending on your background, you might also include sections on publications, awards, or relevant volunteer work.

Tailoring Your Resume

Employers want to know that you are enthusiastic about the role, have researched and assessed the company for fit, and see yourself thriving there based on your expertise and experiences.

To customize your resume for each application, carefully read the job description and note the specific skills and qualifications the employer is looking for. Use some of the same verbiage in your resume to highlight applicable skills and relevant experiences that align with these requirements. For instance, if a job listing emphasizes “proficiency in Python,” make sure to feature your Python-related projects prominently.

Data Analytics Resume Example

Consider how this sample resume blends specific, quantifiable details with relevant skills.

Jane Doe
Phone: (123) 456-7890
Email: janedoe@example.com
LinkedIn: linkedin.com/in/janedoe

Professional Summary
Results-driven data analyst with 3+ years of experience in transforming complex data into actionable insights. Proven expertise in SQL, Python, and machine learning with a track record of driving business growth through data-driven decision-making.

Education
Data Analytics Bootcamp, General Assembly, Year

Experience
Data Analyst
ABC Corp, January 2021 – Present

Developed and maintained dashboards to track key performance indicators, leading to a 15% increase in operational efficiency.
Utilized SQL and Python to analyze sales data, providing actionable insights that resulted in a 10% revenue growth.

Skills

SQL
Python
Machine Learning
Data Visualization (Tableau, Power BI)
Statistical Analysis

Projects

Sales Forecasting Model: Created a predictive model using Python and machine learning techniques to forecast sales, achieving a 90% accuracy rate.
Customer Segmentation Analysis: Conducted clustering analysis to segment customers, enhancing targeted marketing strategies.

Certifications

Data Analytics Short Course, General Assembly
Python Programming for Data Science, General Assembly

How to Build a Data Analytics Portfolio

Most data analysts showcase their portfolios by creating professional websites with SquareSpace, WordPress, or GitHub Pages. Tools like Tableau, Power BI, and Jupyter Notebooks can also help you build and present your projects in a compelling way, demonstrating your ability to apply theoretical knowledge to real-world challenges.

Benefits of a Strong Portfolio For Data Analysts

A strong portfolio differentiates you from other candidates by providing tangible proof of your analytical abilities, problem-solving approach, and creativity. Employers can see firsthand how you collect, analyze, interpret, and present your findings.

Complementing Your Portfolio with a Resume

Your portfolio showcases all the projects you’ve worked on, certifications, and case studies. You can also incorporate instructor or client feedback, reviews, and ratings to highlight your commitment to customer satisfaction.

Your resume includes your background, education, and experience to provide additional context about your full career journey. Together, your portfolio and resume create a comprehensive narrative of your qualifications and capabilities. Make sure both elements complement each other, aligning your showcased skills and experiences for a cohesive presentation to potential employers.

Data Analytics Portfolio Ideas & Examples

To get you started, here’s some inspiration:

Vallabha’s data analytics portfolio showcases a linear timeline of predictive modeling projects as well as a graph that breaks down technical proficiencies in a quick, digestible way.

The “About Me” section shares her passion for finding patterns in complex datasets, while revealing clues about the background experience, certifications, and soft skills that make her good at what she does.

This presentation landed her a role as Lead Data Engineer at M&G, a business process outsourcing firm in Mumbai.

Tim Hopper’s SquareSpace data analytics portfolio is a breath of fresh air in the world of data analysis. It’s not just about showcasing projects—Tim’s site offers a dynamic mix of articles, videos, and podcasts, all tied together with sleek sidebar navigation.

The eye-catching graphics, which nod to the playful “Mr. Men” characters, add a touch of nostalgia to a modern design. This creative approach demonstrates that a standout personal brand can elevate your profile beyond the conventional, proving Tim’s expertise as both a data scientist and a compelling communicator.

Since starting his data analytics career 12 years ago at nonprofit research firm RTI International, he has transitioned to a Lead Software Engineer role at Varo Bank. This journey highlights how mastering the right technical skills and curating an impressive portfolio can open doors to diverse career opportunities.

Downloadable Portfolio Templates

To streamline the process of building your portfolio, you can also use a data analyst portfolio template, available at sites like:

Tips for Showcasing Your Projects

An effective data analytics portfolio includes projects, case studies, and data analyses.

Data Analytics Portfolio for Beginners

Whenever possible, focus on projects that demonstrate your ability to handle tasks similar to those in the job description and highlight achievements that are directly relevant to the job you want.

If you’re just starting out or switching to the field, you may not have a ton of data analytics experience to share. But it’s worth noting that your hands-on coursework counts—and a number of careers use data analysis skills. Seemingly unrelated experience can demonstrate transferable skills relevant to data analysis, such as problem-solving, critical thinking, or communication.

When describing projects, be clear about your role and contributions. Use results to showcase your impact. For example, instead of saying “worked on sales data analysis,” say “analyzed sales data to identify key trends, resulting in a 10% increase in revenue.”

Highlight a variety of projects that showcase different aspects of data analytics, including:

Predictive modeling to demonstrate your ability to identify future trends or behaviors.
Data cleaning and transformation to highlight your data prep and analysis skills.
Data visualization to communicate complex data insights.

Sections of your portfolio might include:

A project section: a brief overview of each project’s tools and techniques
Case studies: descriptions of a problem, your approach, and the outcome
Interactive dashboards: portals that let potential employers explore your work firsthand
Training: a listing of courses taken, certifications, and acquired skills
About me: a section about your passions, background, and data-driven perspective
Resume: an uploaded copy of your resume for added context into your experience

Building Your Online Presence

For data analysts, platforms like LinkedIn and GitHub help promote your portfolio and connect you with industry professionals. Here’s how you can make the most of these resources:

Create a strong profile: Ensure your LinkedIn profile is complete with a professional photo, compelling headline, and a detailed data analytics skills resume.
Showcase your work: Use the “Featured” section to display your portfolio, including links to projects, case studies, and relevant articles.
Engage with the community: Join data analytics groups, participate in discussions, and share insights or articles to increase visibility and network with peers and recruiters.
Ask for recommendations: Request recommendations from colleagues or clients who can vouch for your skills and contributions.

GitHub

Build a portfolio repository: Create a dedicated repository for your data analytics projects. Include well-documented code, explanations, and visualizations to demonstrate your analytical capabilities.
Organize your projects: Use GitHub’s features to categorize your projects into separate repositories or folders, making it easy for visitors to navigate and assess your work.
Contribute to open source: Participate in open-source data analytics projects to showcase your collaborative skills and contribute to the community.
Use README files: Provide context with detailed project README files that explain the problem you solved, the tools used, and the results achieved.

Data Analytics Resume Building & Profile Optimization Techniques

To attract recruiters, make sure you:

Update your profile content: Use keywords relevant to data analytics to improve searchability. Keep profiles fresh with new projects, certifications, and skills. Share your own content, such as blog posts or project updates, to keep your profile active.
Network: Connect with industry professionals, join data analytics groups or forums, and engage with industry leaders to build your network and stay updated on industry trends. Follow and interact with companies you’re interested in or where you’d like to work.
Review your engagement strategy: Like, comment on, and share relevant content from people in your network. Share your own projects, including key visualizations and insights. Write and publish LinkedIn articles on topics related to data analytics, such as case studies, industry trends, or tutorials. Ask and answer questions to demonstrate your knowledge and expertise.

Join General Assembly’s Network

With your future data analytics career on the line, crafting the perfect resume and portfolio can seem overwhelming. Enrolling in General Assembly’s Data Analytics Bootcamp equips you with the tools and support to shine. You’ll get hands-on guidance from career services specialists and a dedicated mentor, ensuring you create standout materials that catch employers’ eyes. Impressively, over 90% of our grads secure a tech role within 180 days of graduating.

This guide, along with our examples and templates, is a great starting point, but why stop there?

Kick off with a free Intro to Coding course online and explore our full-time or part-time bootcamps to begin your journey toward your dream career in just three months. With General Assembly’s world-class support, you’ll engage in meaningful projects, receive invaluable mentor feedback, and build a powerful resume and portfolio that sets you apart in the fast-paced field of data analytics.

Explore our courses:

And visit General Assembly’s blog for more insights and career resources.

Wondering if a career in data analysis is a good move for you? Sign up for our next info session to learn more.

How to Build a Standout Data Analyst Resume and Portfolio