Having a well-organized and impactful data science portfolio can make all the difference in getting noticed by potential employers. But deciding where and how to showcase your data science projects on your resume can be a challenge. Think of your resume as a puzzle, and your data science projects as the pieces that complete the picture of your skills and experiences.

The goal is to place the pieces in a way that showcases your strengths, highlights your achievements, and makes it easy for employers to understand your capabilities without causing confusion - and without losing any of the puzzle pieces.

Finding the Right Approach for Your Data Science Resume

This article will review multiple approaches to placing projects on your data science portfolio and provide the pros and cons of each. Whether you're a seasoned professional or just starting out in the field, the advice in this article is specifically shaped for established professionals looking to make a big career move in data science.

It's important to note that no strategy can replace what I call "the distributed portfolio strategy" which is a comprehensive approach to building and showcasing your portfolio across multiple platforms. I often about the "distributed portfolio strategy" on this blog. The distributed portfolio strategy is a topic I also discussed in the book How to Become a Data Scientist: A Guide For Established Professionals.

Placing data science projects at or near the top of your resume

Leading with your projects can be a great way to showcase your skills and achievements, especially if you're new to the field and your job history doesn't yet demonstrate your data science expertise. Showcasing your projects at the top of your resume allows you to lead with your best work and demonstrate your capabilities right off the bat. However, this approach can also be problematic. Automated applicant tracking systems (ATS) may mistake a project listing as a job, and human readers may also make that mistake. This confusion can cause your resume to be overlooked or dismissed.

Placing data science projects in the summary section

Mentioning a project in your summary section can be a great way to highlight your skills and achievements related to the job you're applying for. This approach allows you to showcase your relevant experience and demonstrate your expertise in a specific area. Keep it to one or two projects and make sure they are closely related to the job or the type of work and topics the employer cares about. Including a hyperlink to an article you wrote or a GitHub repository can also help demonstrate your skills and experience.

Placing data science projects at the end of your resume

Placing your data science projects at the end of your resume can provide a more in-depth look at your skills and experience. This approach can give employers a better understanding of your capabilities, while avoiding overwhelming them with too much information at once. However, this approach also has its drawbacks. By placing your projects at the end of your resume, you risk having them overlooked or not receiving the attention they deserve. It's important to weigh the pros and cons and decide what approach works best for you and your career goals.

Consider placing data science project references in your cover letter

Including project references in your cover letter (if you will have one) can be a great way to provide additional context and highlight the impact of your work. This approach allows you to elaborate on your projects and provide a more comprehensive understanding of your skills and experience. However, it's important to make sure the references are relevant to the job and that your cover letter is concise and to the point.

Conclusion

In conclusion, deciding where to place projects on your data science portfolio requires a thoughtful and strategic approach. Each approach has its pros and cons, and you may find that a combination of approaches works best for you. Whether you choose to lead with your projects, mention them in your summary section, or include references in your cover letter, the goal is to showcase your skills and experiences in a way that highlights your achievements and demonstrates your value as a data science professional.

Like assembling a puzzle, placing projects on your data science portfolio requires finding the right fit for each piece. Remember, no advice, including this article, can fully substitute thorough research and informational interviews with the company you're applying to. Asking for opinions on how a company likes to see projects on a resume can be a valuable way to tailor.