Building Your Data Science Portfolio with DataCamp Workspace (Part 1)

When searching for a job in the data field, your resume or CV will only tell employers so much. It’s one thing to claim to have strong communication skills, proficiency in multiple coding languages, and advanced knowledge of machine learning workflows. It’s quite another to demonstrate these qualities with a selected body of your work. 

A data science portfolio is a powerful tool for showcasing your work and providing concrete evidence of your abilities. In this article, we will cover the types of projects you can use in your portfolio and advice on building one. In follow-up articles, we will go into greater detail about how to create two portfolio projects: an analytics project and a machine learning project.

What is a Data Science Portfolio?

A data science portfolio is a curated collection of one or more projects that you use to demonstrate your data skills. These projects can take various forms and are not limited to technical analyses. Below are some examples of projects you can consider including in your portfolio.

Portfolio project examples

• Analyze a sales dataset, providing insights on trends, sales figures, and popular products with a heavy emphasis on visualization.
• Perform an A/B test analysis to compare the effectiveness of an intervention in a business setting.
• Use online product reviews to perform natural language processing tasks like sentiment analysis or topic modeling.
• Use a dataset to create a data application, such as an interactive dashboard.
• Create a video tutorial on a new package or tool.
• Write an article on a trending topic in the data field.
• Use a machine learning algorithm to solve a particular problem, such as predicting customer churn or segmenting customers.

This is by no means an exhaustive list. As we will cover in the next section, you should choose projects tailored to your skills and the roles you are targeting.

Tip: To brush up on your knowledge, be sure to browse our catalog of DataCamp courses!

What Makes a Good Portfolio?

A portfolio should demonstrate your best qualities and be relevant to the roles you are applying for. Here is a list of 5 things to remember when creating your portfolio.

Cover a range of abilities

For most data-related jobs, you will do more than just crunch numbers. You will need to perform tasks such as visualizing results, communicating findings, and mentoring  others. Ensure that your portfolio shows you are versatile and strong across the board. This does not mean every project has to contain every skill you possess. Your abilities can be distributed across your portfolio.

Keep your audience in mind

Who is going to be reviewing your portfolio? Your projects should be accessible to less technical audiences while also providing enough depth for those who do possess technical skills. Your projects should be easy to follow and provide a pleasant user experience. A clear introduction describing the purpose of the project and the methodologies used can be a great way to guide readers through your work.

Curate your portfolio

A portfolio is not a parking lot for every data project you have ever started. The projects in your portfolio should be strong examples of the professional output employers could expect from you. Each piece of work should be carefully reviewed and edited until you are satisfied that an employer would be impressed with your abilities. Aim for a fewer number of high quality projects.

Be memorable

It is easy to be lost in a sea of projects. Make sure your projects create a lasting impression on readers. Your projects should tell a compelling story, with a clear motivation and question/problem you are trying to solve. Ensure that your projects are aesthetically pleasing and show off a personal style. Finally, create content that is  new and exciting. Another analysis of Titanic survivors is unlikely to spark someone’s curiosity.

Highlight relevant skills

Your portfolio should match what you include in your CV and align with the skills required for the roles and opportunities you are pursuing. If you are applying for a role that requires strong knowledge of SQL, at least one of your projects should contain evidence of this. If you claim to have excellent data visualization skills, take extra care that your visualizations are top-notch.

Want some more tips on how to develop a strong portfolio? Be sure to check out a previous blog entry which goes into even greater depth on how to make your work shine, including tips on storytelling and your personal brand.