Tips for Crafting an Impressive Data Analyst Portfolio

Having a project portfolio is an industry standard for data analysts. Making it impressive is not easy, but we’ll provide plenty of tips to make it easier.

The main question, when job-searching is how to stand out. In a good way. Standing out in the wrong way is very easy – writing a CV awash in typos and piss poor grammar or a cover letter for a data analyst job at Apple, but keep referring to the company as Microsoft should do the trick.

Having a project portfolio, maybe that’s how you positively stand out? Not quite. Long and gone are days when simply having a portfolio could wow the interviewers. Nowadays, portfolios are like tattoos – everybody has them, and they’re mostly terrible. Not having any tattoos is how you stand out! By the same token, is not having a project portfolio the most efficient way to stand out when job searching positively? It would be if the companies appreciated absurdist humor and were open to being trolled. Unfortunately, most are not.

So, you’ve got to play the game by the rules and stand out at the same time, which is the hardest thing to do. The only way to do this is by using quality as your main weapon: craft a data analyst portfolio and make it impressive.

Why would you be interested in knowing how to do all that? First, most companies require it, so there is no arguing with that! Second, a portfolio of projects is probably the most efficient way to showcase your practical data analysis skills to employers before they even meet you.

Before getting to the bottom of what makes an impressive data analyst portfolio, I should clarify who data analysts are and what they do.

Who Are Data Analysts? And Their Growing Demand

The exponential growth of the collected data and what’s possible to do with it has caused the rise of the so-called data-driven companies. Those are companies that ‘discovered’ making decisions on objective data is much, much better than the good, old put-the-finger-in-the-air method. Who would have thought?

This technological change of recent years and decades has impacted every industry, from education, healthcare, and NGOs to finance, marketing, engineering, agriculture, and hospitality.

With that came a growing demand for experts who can make use of that data. Enter data analysts.

Who are they? That’s easy! They are people who analyze data! Thank you, Captain Obvious. Here, have a lollipop.

To elaborate, the data analysts’ job is to collect, clean, and analyze data. By doing so, they get insights from data, which helps businesses solve problems and make astute strategic decisions. Those insights have to be presented to decision-makers – who are often less technically savvy – via reports and visualizations; that’s something that data analysts also do.

The Tools Data Analysts Use

Data analysts use lots of technology, such as programming languages and other software and tools, in their work.

Programming languages: The two most common programming languages data analysts use are SQL (in databases such as PostgreSQL, SQL Server, or MySQL) and Python. SQL is used for database querying, manipulation, and some data analysis. Python is commonly employed in data cleaning, manipulation, computation and statistical analysis, and data visualization.

Occasionally, they might use R, a programming language for statistical analysis and data visualization.

Spreadsheets & BI Tools: Spreadsheets like Excel and Google Sheets are commonly used, as they are great for data manipulation, visualization, and some simpler analysis without code writing.

BI tools like PowerBI, Tableau, QlikSense, and Looker Studio are used to present data analysis insights in the form of reports, charts and diagrams, and (interactive) dashboards.

Statistical Software: It’s also possible that, in certain cases, you’ll have to use statistical software, such as SPSS and SAS.

Big Data & Data Mining Tools: If you’re working with big data, you’ll need knowledge of certain tools to handle it. Some examples are Apache Spark, Apache Hadoop, RapidMiner, and KNIME.

Version Control Tools: Tools like Git allow you to track code and dataset changes. This makes your project reproducible by others, and it’s easier to audit. Version control tools have another essential purpose, which is enabling collaboration with others on the same project,

The Role of a Data Analyst

The data analyst job description I mentioned earlier stays more or less the same for every data analyst in every company. What changes is a data analyst’s role or purpose in the company, all depending on the tasks a data analyst is employed on.

Here are some of the roles a data analyst can perform.

Find more about each role in our What is a Data Analyst article.

The Importance of Having a Well-Crafted Portfolio for Data Analysts

As the high demand for data analysts is not a new thing, the supply of data analysts has caught up with it in the meantime. This means there’s a high demand for data analysts, but many people are also attempting to switch careers as they heard being a data analyst is such a cool thing. And it gets you a data analyst salary, which can be pretty handsome.

If you are an experienced data analyst, proving your data analysis skills is relatively easy, as your experience speaks for itself. Even then, it helps to have something that really proves your work experience equipped you with relevant skills.

Imagine how hard it is if you don’t have any work experience at all.

Having a portfolio of data analysis projects is a brilliant solution for both situations. It’s a great way of showing technical and other relevant data analysis skills. However, portfolios have become an industry standard; you will have one, that person over there will have one, and everybody else applying for the job will have one.

The importance of having a well-crafted portfolio today is higher than ever and the most effective way to stand out from the crowd.

Key Elements of an Impressive Data Analyst Portfolio

To present your skills and impress your future employer most effectively, include these elements in your data analyst portfolio structure.

1. Introduction

The portfolio introduction gives an overview of your professional background, interest in data analysis, and the portfolio itself.

The overall style shouldn’t be too verbose; stick to the clear and concise style.

For example, you could write something like this:

“Welcome to my data analyst portfolio!

With a background in computer science and economics, uncovering insights from data is my profession and my passion.

This portfolio consists of 5 projects that showcase practical applications of my data analysis skills. Due to my interest in finance, most projects focus on financial data analysis.”

2. Personal Introduction

In a personal introduction, you should talk about how you became a data analyst, where you currently are in your career, and mention relevant experiences.

Here’s how you could formulate this section:“My name is Wilson Burke, and I am a data analyst by profession and passion.

In 2023, I gained a bachelor’s degree in computer science and economics at Boston University. Recently, I finished a 1-year internship as a data analyst at JPMorgan Chase’s Asset Management Division. During that time, I helped analyze investment strategies and develop financial models for asset management.

I analyzed market trends and economic indicators to inform investment decisions. I also performed scenario analysis and stress testing to assess investment risks.

The tools I used for my work include:

• SQL – for data collection, manipulation, and basic analysis
• Python (pandas & NumPy) – for data cleaning, manipulation, and advanced analytics
• Power BI – for creating reports, interactive visualizations, and dashboards
• Bloomberg Terminal – for market research and investment strategy development

This experience makes me want to specialize further in financial analysis and risk management.”

3. Project Descriptions

Notice the plural in the above heading. You should write a separate description for each project. The descriptions should include a brief description of the business problem you tackle with the project, the methodologies you used, and the conclusions you reached.

For example:“Project: Portfolio Performance Analysis

Description: I analyzed the investment portfolio of the bank’s asset management division to uncover performance trends and optimize asset allocation.

I cleaned and processed data using SQL (in PostgreSQL) and Python. Then, I used Power BI to visualize portfolio performance metrics across asset classes, industries, and time periods.

The analysis showed that, compared to other asset classes, equities performed better during a bullish market, with an average annual return of 12%. On the other hand, bonds can provide stability during downturns, with a consistent but much lower annual return of 4%.

The technology and healthcare sectors showed strong growth, significantly impacting the overall portfolio returns. Their contributions to portfolio returns were, on average, 27% and 16%, respectively. Conversely, the energy sector was lagging behind, so investment reallocation should be considered. Its return contribution is 3%.

Geographical diversification of a portfolio showed to be a great contributor to gains. Portfolios that are exposed to emerging markets have, on average, 38% higher returns compared to portfolios focused solely on developed markets.“

The Treadmill Buyer Profile, Marketing Campaign Results, and Insights from Failed Orders are good examples of how to set up an assignment and discuss data, practicalities, and assumptions. You can also include these projects in your portfolio, as well as others from our Data Projects page.

4. Technical Skills

The projects you include in the portfolio should show data analyst technical skills. But I recommend that you be even more explicit and help potential employers understand how good your skills are.

By that, I mean include a section where you list the technical skills you employed and explain how you used them in your projects.

This section could be divided into two subsections:

• Programming Languages
• Tools and Software

Programming Languages: Here’s an example of what you can write here.

“ 1. SQL – Used to query data in relational databases. For example, I used it to extract financial data and create reports on asset performance.

2. Python (pandas, NumPy, Matplotlib) – It was used for data cleaning and preparation (pandas), numerical operations (NumPy), and trend charts (Matplotlib).“

Tools and Software: This subsection could look like this.

“1. Power BI – I leveraged it to create charts and interactive dashboards that showed asset classes, their geographical distribution, risk metrics, and returns.

2. Excel – This tool was useful in financial modeling and simulations of investment scenarios. For example, I extensively employed array formulas, data analysis, and statistical and logical functions.

3. Git – Used Git for version control, so all code and data analysis changes are tracked.“

5. Data Visualization

Each project in a well-crafted data analyst portfolio should have a data visualization section.

Visualizing data is a crucial data analyst’s skill, so you should strive to showcase it in your projects. To do it well, try to make your visualizations as visually appealing and creative as possible, but keep in mind that they should also be clear, insightful, and easy to understand. After all, data visualization is not that much about your design skills but about providing information in a visual format.

6. GitHub Repository

You call yourself a data analyst while not having a GitHub profile? Quick, go here and create one. GitHub is basically an industry standard for hosting your project and presenting its code. It allows potential employers to access it and assess your coding skills easily.

Tips for Creating and Presenting Projects

While the portfolio elements I discussed earlier are extremely important, they won’t help if your portfolio lacks the essence. The essence of every data analyst portfolio should be the projects.

Make sure that your projects and presentations are high-quality. Here are some tips on how to achieve that.

1. Selecting Projects

When selecting projects, focus on quality, not quantity. If the projects are of high quality, there’s really no need to have more than 3-5 projects in a data analyst portfolio.

What do I mean by high quality? First of all, the projects should solve real-world problems, as this will be your job as a data analyst.

Second, high quality projects are those targeting industry and skills of a particular job description. For example, if you’re targeting working in financial services, there’s really no point in doing an inventory management project.

Finally, your projects should be as diverse as possible in terms of skills showcased. For example, do one project that requires a lot of data cleaning, the second asks for high-level expertise in data analysis and statistics, and the third one that focuses on data visualization. Or alternatively, do projects that are all data cleaning, analysis, and visualization-heavy, if you can find them.

If you want to go the extra mile, tailor separate portfolios to the requirements of different positions you apply for.

2. Storytelling

You should tell a story with your data, not just boringly dump tables and metrics on your audience.

Start by explaining the problem and contextualizing it. Then, describe how you approached your analysis and what tools you used. Finish with insights and recommendations that refer back to the problem you started with. That way, you can create a narrative arc.

Be aware that in real life, your data analyses will most likely be used by non-technical people. So, when telling a story with your data, keep technical jargon to a minimum. You’ll probably have to use some technical terminology, but when you do, make sure you always explain it and do so in an understandable way.

All in all, data storytelling should be engaging. To achieve that, try to make it more personal, use anecdotes, metaphors, and analogies, and extensively use data visualizations.

3. Visual Appeal

Humans are highly visual creatures, with ca. 80% of the information their brain receives coming from sight. You shouldn’t disregard that; a visually appealing portfolio can boost its impact. (With the assumption all other key elements of a good data analyst portfolio are in place.)

Using clean and professional formatting is recommended. Equally important: once you choose formatting, stick to it. Trust me, consistency is sexy!

This also applies to data visualizations; use consistent and easy-on-the-eye color schemes, fonts, and layouts. Try to find a middle ground; don’t make your visualizations look dull, but they also shouldn’t look like they came from the 1969 San Francisco tripping on LSD.

4. Feedback and Iteration

Having a clear idea about what your data analyst portfolio should look like and being confident about nailing it is commendable.

Yes, this is your portfolio, but keep this in mind: no matter how good a data analyst you are, many people are still better than you. I’m not saying this to put you down. I simply want to encourage you to use this as an opportunity to learn and seek feedback from your peers and other professionals.

For example, you can send your portfolios to more experienced data analysts you know. In fact, you don’t even have to know them; GitHub is crowded with data analysts and other data professionals. They can share feedback on your work, suggest alternative approaches and improvements, or discover some mistakes and inconsistencies that you missed.

Use that feedback to improve your portfolio.

Online Platforms for Hosting Portfolios

I recommend three platforms for hosting portfolios – no need to put a portfolio on all three (you can, if you want!). Usually, one is enough, and then you share a link on the other platforms you use.

1. GitHub

GitHub is a platform designed for sharing code, collaboration, and version control. This makes it ideal for hosting a data analyst portfolio.

On GitHub, you can create repositories for each project where you’ll have README files explaining the project, its goals, technologies used, and instructions for running the code.

With GitHub Pages, you can host a static site directly from your repositories, which you can use as a portfolio site showcasing your projects.

2. LinkedIn

Another good idea is to share your portfolio on LinkedIn. You can do that in the Projects section of your LinkedIn profile, where you can share links to your portfolio and repositories.

You can also write posts in which you share project updates and discuss methodologies, technologies, and insights.

Also, don’t forget to leverage LinkedIn's primary purpose. Use it to connect with other data professionals, participate in discussions, and join relevant groups. This can be an opportunity to learn but also to expand your network, which can also help you find your next data analyst job.

3. Personal Website

This is, of course, the most elaborate approach but also the one that gives you the most freedom in what you want to present and how.

A personal website can be a vehicle for creating your personal brand. You can customize your design and have sections where you present yourself, your portfolio, and individual projects. You can also add the blog section, where you write articles about new industry developments, explain different data analysis concepts, or write opinion pieces.

If you want to get real fancy, add interactive features, such as project demos and contact forms, for potential employers to beg you to work for them.

Final Touches and Presentation

Your data analyst portfolio now really looks fantastic! You should just add some final touches, and you’re done.

1. Importance of Error-Free Content

I cun’t strees dis enugh; you’re porto folio shoold bee with out gramattical erours and typhus.

Boy, did reading the previous sentence hurt, didn’t it? Imagine then having a data analyst portfolio filled with such sentences. Don’t let your project look like that! Proofread your content multiple times and run it through a spell checker. Weeding out grammatical errors and typos contributes to your portfolio looking professional and demonstrates your high level of attention to detail.

2. Maintaining a Professional Tone and Presentation

The writing style and tone should be professional throughout your portfolio. Of course, you’re allowed to imprint your personality, but stay professional by mainly using formal language; avoid using slang and excessively casual language.

3. Ensuring the Portfolio is Accessible and Easy to Navigate

The data analyst portfolio should have a clear structure and allow intuitive and user-friendly navigation. Make your content accessible to all users.

One such example is using clear and readable fonts to help the visually impaired.

Also, you can use a blue/orange palette, Paul Tol’s Qualitative Color Schemes, patterns, and textures to make data visualizations accessible to people with color blindness.

Another example of making your portfolio accessible is by providing transcripts for your videos to help hard-of-hearing people.

If you provide descriptive alt text for all images, screen readers will be able to describe the images to visually impaired people.

Conclusion

In conclusion, investing time and effort in crafting an impressive data analyst portfolio is one of the most effective ways to stand out from other job candidates. Think of it as your very elaborate business card.

Creating a portfolio that will draw the attention of potential employers is not easy, though it should be easier after you’ve read all those tips above. They boil down to having a clear structure where you introduce yourself and your portfolio, describe every project, discuss technical skills, visualize analysis insights, and share the project code.

You should also pay attention to the quality of the projects you choose and how you present and host them.

A data analyst portfolio really is an effective tool for showcasing your data analysis skills; no wonder employers have started requiring it.

If you don’t yet have a portfolio, I encourage you to create one by following these tips. You can also use them to improve your existing portfolio and if it brings you more success in job-searching.