How to Build a Data Analyst Portfolio: Tips for Success

As you begin your data analyst job search, your portfolio may be one of the most important aspects of your application. Your portfolio showcases your skills at work in the real world. This validates your skills to recruiters, hiring managers, and potential clients in a way that’s hard to do with a resume alone.

In this article, we’ll discuss how to build your data analyst portfolio, even if you don’t have any job experience. We’ll go over free and paid platform options, as well as the types of projects you should include to make your portfolio shine. 

Build your portfolio with project-oriented data analysis courses

The Google Data Analytics Capstone: Complete a Case Study course offers insight into how to build a data analytics portfolio and provides guidance for creating a compelling case study.

In IBM's Data Analyst Capstone Project course, you'll assume the role of an Associate Data Analyst and apply your data analytics skills to solve a theoretical business challenge, culminating in a portfolio-worthy report for stakeholders within a fictional organization.

How to build a data analytics portfolio

While you can list your data skills on your resume, it’s your portfolio that provides the proof. In its simplest form, a portfolio is a collection of data projects you’ve worked on.

Below, we take a closer look at how to build one. If you'd like to learn more about the importance of portfolio projects in the job search, though, watch this lecture from Google's Data Analytics Professional Certificate first:

Portfolio Platforms

The first step in building a data analytics portfolio is choosing where to host it. 

You don’t have to spend a lot of money or build your own website from scratch, either. When you’re just getting started, consider these free portfolio website options:

• LinkedIn: LinkedIn makes it fairly easy to add, update, and remove projects from your profile, which can double as an online portfolio. The platform supports a range of formats (.jpeg, PDF, PowerPoint, Word, and others), so you can upload and share many types of content. With LinkedIn, you can add projects under your Featured, Experience, or Education sections.

• GitHub: Another popular option where you can host your portfolio for free is GitHub, an open-source community of some 56 million developers. Once you create an account, you can start adding data projects to a public repository to show off elements like your code and Jupyter Notebooks.

• Kaggle: Kaggle, a customizable Jupyter Notebooks cloud environment, can also serve as a free portfolio of your work. Here you can display the results of any Kaggle data science competitions you take part in or showcase any data sets you’ve built or code you’ve written.

As you gain experience and your portfolio continues to grow, you may want to consider moving it to a standalone website. Host your portfolio through services like SquareSpace or Wix that feature pre-made templates and easy drag-and-drop modification. If you’re comfortable working with HTML, you can host your site through WordPress for even more flexibility. 

Tip: Many data analysts upload their work to GitHub and link to it from their LinkedIn profile, resume, or personal website. Your work may even catch the attention of a recruiter.

What to include in your portfolio

The contents of your portfolio are more important than where you choose to host it. A simple portfolio should include at least two sections, an “About me” section, and data analytics projects. Let’s take a closer look at both.

About me

The “About me” page gives you an opportunity to introduce prospective employers to who you are, what you do, and why it’s important to you. You can use this section to explain:

• How you got started in data analysis

• What about data interests you most

• Where your passions lie in relation to data analytics

This is also a great place to include your contact details (if you don’t have them on a separate page) and links to your social media accounts.

Projects

The bulk of your portfolio will likely comprise a series of projects and case studies that demonstrate your key skills. In general, your portfolio should showcase your best or latest work. Try to include projects that highlight your ability to:

• Scrape data from websites: Show your code, and use hashed comments to explain your thinking.

• Clean data: Take a data set with missing, duplicate, or other problematic data, and walk through your data cleaning process.

• Perform different types of analysis: Use data to perform diagnostic, descriptive, predictive, and prescriptive analyses.

• Visualize data to tell a story: Create a chart, map, graph, or other visualization to make your data easier to understand.

• Communicate complex ideas: To highlight your communication skills, consider writing a blog post that outlines your process or explains a difficult data concept.

• Collaborate with others: If you’ve worked on a group project, be sure to include it.

• Use data analysis tools: Share projects that show off your ability to use SQL, Python, R, Tableau, etc.

What do I put in my portfolio if I don’t have work experience?

If you’re just starting out and don’t yet have work experience as a data analyst, include projects you’ve completed on your own or as part of your coursework.

Start with small projects, and add them as you go. Once you learn how to scrape a website, for example, you can add a screenshot of your code, as well as a short paragraph explaining what you did. 

Read more about how to become a data analyst.

Other items to include

While you’ll want to include an “About me” section and some projects, you can also build out your portfolio with some other elements.

• Blog: As you work on projects, consider writing blog posts about your process and findings. This can be an excellent way to showcase your communication skills while reinforcing your learning.

• Testimonials: If you can gather a few quotes from professors, employers, clients, or colleagues about your work in data analytics, it’s a good idea to include them. 

Data analyst portfolio tips and best practices

Ensure your data analyst portfolio is as polished as possible with these tips and best practices:

1. Use your portfolio to demonstrate your passions. 

Your portfolio is an excellent spot to communicate what gets you fired up. Are you passionate about climate change? Prioritize projects using climate data. Are you interested in a job in the health care industry? Include health informatics projects.

2. Take advantage of tools like Jupyter Notebook and R Notebook. 

Humans are visual creatures, so try to make your portfolio more than just a wall of text. One way to do so is by using R or Jupyter Notebooks. These web applications allow you to share your live code, visualizations, and text in an interactive way.  

3. Only include your best work. 

When it comes to your portfolio, less is more. When you’re just getting started, you might include every project you’ve worked on. But as you gain experience, you’ll want to include just enough to demonstrate your skills.  

4. Build your portfolio as you learn. 

You don’t have to wait for your first job to start developing your portfolio. If you’ve taken classes in data analytics, chances are they included some assignments or course projects. Add those to your portfolio. If you’re learning independently, start completing small portfolio projects as you go. You’ll not only practice your new skills, but you’ll also have material for your portfolio.

5. Browse other portfolios for inspiration. 

Spend some time looking at other data analyst portfolios. You might pick up some ideas for how to present a certain type of project or how to incorporate a certain skill. 

Check out more data analytics project ideas that you can use as you build your portfolio.

How to present your portfolio during an interview

As you interview for data analyst jobs, you may be asked to discuss case studies from your portfolio. Watch this video to learn more about how to prepare elevator pitches for each of your case studies to effectively highlight your skills.

Get started on Coursera

Complete portfolio-ready projects as you work your way through these popular data analysis programs:

• Complete hands-on projects for your portfolio and practice using industry-standard tools like SQL, Tableau, and Python with the Meta Data Analyst Professional Certificate.

• Deepen and demonstrate your Python capabilities with the University of Michigan's Python for Everybody Specialization.

• Build custom reports and dashboards with Power BI with the Microsoft Power BI Data Analyst Professional Certificate.