A Data Science Portfolio is More Valuable than a Resume

Benjamin Obi Tayo Ph.D.

Nov 20 · 5 min read

Data Science is a practical field. Hands-on skills are very important especially when you are interested in working outside academia as a practicing data scientist.

In academia, you need more theoretical and research skills. While in-depth knowledge in the theoretical foundations of data science is important, as a practicing data scientist, hands-on experience is very crucial and one way to showcase you hands-on skills is via building a very great portfolio. Companies interested in hiring you would definitely be asking you for a portfolio, as it gives evidence of your strengths in fundamental data science concepts.

While a data science resume is important, a data science portfolio is more valuable than a resume. This article will discuss 3 important platforms that can be used for portfolio building.

Before delving into the topic of building a good data science portfolio, let’s first discuss 5 reasons why is a data science portfolio important.

5 Reasons why a data science portfolio is important

1. A portfolio helps you showcase your data science skills.

2. A portfolio enables you to network with other data science professionals and leaders in the field.

3. A portfolio is good for bookkeeping. You can use it to keep a record of your completed projects, including datasets, codes, and sample output files. That way, if you have to work on a similar project, you can always use code that has already been written, with only minor modifications.

4. By building a portfolio and networking with other data science professionals and leaders, you get exposed to technological changes in the field. Data science is a field that is ever-changing due to advances in technology. To keep up with the latest changes and developments in the field, it is important to join a network of data science professionals.

5. A portfolio increases your chances of getting a job — I’ve d had numerous opportunities from LinkedIn, for instance recruiters reaching out to me for job opportunities in data science.

Lets now discuss 3 important platforms for creating a data science portfolio.

Platforms for building a data science portfolio

1. Github

Github is very useful platform for displaying your data science projects. This platform enables you to share your code with other data scientists or data science aspirants. Employers interested in hiring you would check your github portfolio to assess some of the projects you’ve completed. So it’s important for you to build a very strong and professional portfolio on github.

To establish a github portfolio, the first thing to do is to create a github account. Once your account is created, you may go ahead to edit your profile. When editing your profile, its a good idea to add a short biography and a professional profile picture. You may find an example of a github profile here: https://github.com/bot13956.

Now let’s assume that you’ve completed an important data science project and you would like to create a github repository for your project.

Tips for creating a repository : Make sure you choose a suitable title for your repository. Then include a README file to provide a synopsis of what your project is all about. Then you may upload your project files including the dataset, jupyter notebook, and sample outputs.

Here is an example of a Giihub repository for a machine learning project:

Repository Name: bot13956/ML_Model_for_Predicting_Ships_Crew_Size

Repository URL: https://github.com/bot13956/ML_Model_for_Predicting_Ships_Crew_Size

README File:

ML_Model_for_Predicting_Ships_Crew_SizeAuthor: Benjamin O. TayoDate: 4/8/2019We build a simple model using the cruise_ship_info.csv data set for predicting a ship's crew size. This project is organized as follows: (a) data proprocessing and variable selection; (b) basic regression model; (c) hyper-parameters tuning; and (d) techniques for dimensionality reduction.cruise_ship_info.csv: dataset used for model building.Ship_Crew_Size_ML_Model.ipynb: the jupyter notebook containing code.

2. LinkedIn

LinkedIn is a very powerful platform for showcasing your skills and for networking with other data science professionals and organizations. LinkedIn is now one of the most famous platform for posting data science jobs and for recruiting data scientists. I’ve actually got 2 data science interviews via LinkedIn.

Make sure your profile is up-to-date at all times. List your data science skill sets, as well as your experiences, including projects that you’ve completed. It would be worthwhile to also list awards and honors. You also want to let recruiters know that you are actively searching for a job. Also on LinkedIn you want to keep up-to-date by following data science influencers and publications such as Towards Data Science and Towards AI. These companies post updates on interesting data science articles on various topics including machine learning, deep learning, and artificial intelligence.

Find an example of my posts on LinkedIn from here: https://www.linkedin.com/in/benjamin-o-tayo-ph-d-a2717511/detail/recent-activity/shares/

3. Medium

Medium is now considered one of the fastest growing platforms for portfolio building and for networking. If you are interested in using this platform for portfolio building, the first step would be to create a medium account. You can create a free account or a member account. With a free account, there are limitations on the number of member articles that you can actually access per month. A member account requires a monthly subscription fee of $5 or $50/year. Find out more about becoming a medium member from here: https://medium.com/membership.

Once you’ve created an account, you can go ahead an create a profile. Make sure to include a professional picture and a short bio. Here is an example of a medium profile: https://medium.com/@benjaminobi.

On medium, a good way to network with other data science professionals is to become a follower. You can also follow specific medium publications that are focused on data science. The 2 top data science publications are Towards Data Science and Towards AI.

One of the best ways to enhance your portfolio on medium is to become a medium writer.

Why should you consider writing data science articles on Medium?

Writing medium articles has 5 main advantages:

1. It provides a means for you to showcase your knowledge and skills in data science.
2. It motivates you to work on challenging data science projects, thereby improving your data science skills.
3. It enables you to improve your communication skills. This is useful because it enables you to convey information in a way that the general public can understand.
4. Every article published on Medium is considered intellectual property, so you can add a medium article to your resume.
5. You can make money from your articles. By means of the Medium Partner Program, anyone who publishes on Medium can make their articles eligible for earning money.

If you are interested in becoming a data science medium writer, here are some resources that can get you started:

Beginner’s Guide to Writing Data Science Blogs on Medium

Choose the Right Featured Image For Your Data Science Articles

In summary, we’ve discussed 3 important platforms that could be used for building a data science portfolio. A portfolio is a very important way for you to showcase your skills and to network with other data science professionals. A good portfolio will not only help you keep up-to-date with new developments in the field, but it will help increase your visibility to potential recruiters.

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WRITTEN BY

Benjamin Obi Tayo Ph.D.

Physicist, Data Scientist, Educator, Writer. Interests: Data Science, Machine Learning, AI, Python & R, Predictive Analytics, Materials Science, Bioinformatics