A data science portfolio is a great way to show off your skills and talents to potential employers. It can be difficult to stand out in the competitive data science job market, but with a strong data science portfolio, you will have an edge over the competition.
In this post, we will discuss three easy ways to make your data science portfolio stand out. Let’s get started!
What does a data science portfolio include?
A data science portfolio is a collection of your work that demonstrates your skills and abilities in data science. These profiles typically include a mix of scripts of code from data science projects you’ve worked on, data visualizations you made, and write-ups on personal projects you’ve completed.
When applying for data science positions, your potential employer will want to see your data science achievements. Employers use portfolios as a way to evaluate candidates, so it is important that your data science portfolio is well-crafted and showcases your best work.
Why is it important for your data science portfolio to stand out?
With data scientist jobs being highly favored among the Gen Z workforce, the competition for such data science roles is starting to heat up. With many pursuing careers in data science, you’ll need to find ways to stand out among the crowd.
Having an excellent portfolio is important for 3 main reasons:
- It acts as an extension of your resume
- It shows expertise in using certain tools
- It demonstrates your problem-solving approaches
Now let me go through some ways you can make your data science portfolio stronger than most others.
What are 3 easy ways for your data science portfolio to stand out?
Your data science profile should be a reflection of your skills and experience.
With that said, here are three easy ways to make your data science portfolio stand out:
- Make it visual
- Include links to popular data science platforms
- Write blog posts to complement your projects
1. Make it visual
Portfolios are one of the major component’s employers look at before starting the interview process in data science.
Much like your resume, employers are likely to spend less than one minute looking at your data science portfolio. To make an impression, your data science documentation should be heavily focused on visuals.
Some data scientists’ portfolios I love are those that use data visualizations to tell a story. Great data visualization can communicate complex information in an easily digestible format.
Here are some guidelines you can follow:
- Ensure it is visually appealing and easy to navigate
- Include screenshots, graphs, and charts to make your data science portfolio pop
- Explain any insights found in the visualizations
Including data visualizations in your profile will help you stand out from the competition and communicate your skills effectively.
Since data visualizations are a big part of data science work, I’d recommend showing off some charts and dashboards you’ve created. If you’ve used Python in any of your data analytics certificates, do include any line charts, bar graphs, and plots you have created using Plotly/Seaborn in your data science portfolio.
If you’ve created some dashboards in Tableau, do publish them on Tableau Public and link that up to your portfolio site. Or if you’re a Power BI user, do take screenshots/GIFs of the dashboard in use and include them in your portfolio.
Source: My Tableau Public profile
Having visuals to represent your work can make a huge impact and will help you stand out from the rest. This is just one example of how you can make your data science portfolio stand out with visuals.
Let’s move on.
2. Include links to popular data science platforms
A strong data science portfolio should include links to popular data science platforms as well. By having links of popular data science tools in your portfolio, your employers would perceive you as having higher credibility.
This credibility comes from the demonstration of your experience and skills since many data science hiring managers use these platforms often themselves.
Some common platforms to link and display your work include:
- GitHub
- Kaggle
- Stack Overflow
- RPubs
- Tableau Public
If you’re someone who has had several machine learning projects done in Python, do upload them to your personal GitHub account so others can read your code. By linking your GitHub repo links to your portfolio, employers can take a glimpse at your coding quality and proficiency!
One tip I’d recommend is to include a README file for your GitHub profile and customize it to showcase the data science skills and programming languages you’ve learned.
3. Write blog posts to complement your projects
The last way to create an outstanding data scientist profile is to document your portfolio projects in writing – via blog posts!
Having comprehensive and concise blog posts on your data science portfolio shows employers your thought process and how you approached each project. This is a great way to demonstrate your problem-solving skills and how you can solve business problems through analytics for your employer.
For example, if you’ve written some scripts in R for your data mining project and would like to help your employers understand the steps you took, writing an accompanying blog post would be perfect. In this case, I’d recommend trying to document everything in Rmarkdown as I did here.
If you’re interested to publish more data science content to further boost your LinkedIn profile as data scientist, do consider these platforms:
- Medium
- TowardsDataScience
- WordPress (your own blog site)
By writing blog posts, you’re able to provide more context and explanation for each data science project in your portfolio. As a result, employers would be able to appreciate your work even more.
Source: My analytics blog, AnyInstructor.com
Conclusion
By following these three easy tips, you can make your data science portfolio stand out from the competition. I hope these tips will help you in perfecting your portfolio and I wish you all the best in your data science career.
Thanks for reading!
Author bio
Austin Chia is the Founder of Any Instructor, where he writes about tech, analytics & software. After breaking into data science without a degree, he seeks to help others learn about all things data science and tech. He has previously worked as a data scientist at a healthcare research institute and a data analyst at a health-tech startup.
