Many people have argued that new hiring practices and the data science field itself have made the resume redundant. Why do so many employers still ask for one? Resumes are a tried and true part of the job search, but old resume styles don’t fit the new tech normal, as a data science portfolio is what’s hot.
A resume is still an essential part of your overall job profile, allowing potential employers to see a job trajectory and education history. Resumes alone are a bit too static for the new job market — enter the portfolio page.
Why build a data science portfolio page?
Where resumes show development and specific skills, portfolios add a dynamic element to your job application. Employers can see successful projects, places where you innovated, or problem solved, your way through an obstacle, and current projects.
Trying to smash everything you’ve accomplished in data science onto a resume can be challenging. How do you demonstrate mastery of coding, visualization, math skills, domain knowledge, and data wrangling all in one place? Add the communication skills to document and discuss your projects and findings, and you’ve got an incomprehensible resume.
A portfolio page puts these accomplishments into perspective. Your potential employer has a full view of the depth and breadth of your knowledge and experience while handling the bullet points in resume form. The two things just go together.
Building a data science portfolio page
A portfolio page should exist on the web so that it’s always current no matter who is looking or when. Choose an online location or stick to GitHub since many potential employers already occupy that space.
Your portfolio must highlight:
- Data Science technical skills
- Knowledge and communication skills
These hard and soft skills create a well-rounded portfolio that tells an employer that you belong on the payroll. Let’s take a closer look at each of these sections.
Data Science technical skills
Completed projects show your employer your accomplishments even if you’ve never been employed in Data Science before. School projects, work projects, and personal ones are all fair game here.
- Personal projects — This is an excellent example of your creativity and initiative. Put the code on GitHub and be sure it’s well commented and clean. Add visualizers using programs like D3.js or Plotly.
- Professional projects — If your company permits you to showcase, this is an excellent example of how you work. Get your company to open source if possible, and be sure to outline both the problem and how you solved it with success.
- School projects — School projects help showcase your learning evolution and could be a good highlight of teamwork since many are group projects. Ensure they’re also well commented and clean.
- Coding competitions — Hackathons and competitions can be highly visible ways to showcase your problem solving and innovation skills. Whether you’ve won or not, showcase these projects.
- Programming skills — Find groups on GitHub to join and start small. Provide clean, well-documented code for your open source contributions and be rigorous. Your employer will be looking for professional-level contributions.
Knowledge and communication skills
Communication skills remain at the top of the list for employer needs. Now that every department is going high-touch for data, data scientists must communicate well outside the department.
There are a few ways to showcase communication skills, but there are two highly accessible options for your portfolio.
Written skills are critical for data scientists in the workplace. Blogs allow you to demonstrate your knowledge by explaining, highlighting, or synthesizing highly complex information.
If you don’t know what to write about, just take a topic you aren’t familiar with and write about what you’ve learned. You can also write a post to clear up a misconception you commonly hear or to offer advice.
Provide links to your blog posts and the basic topic so that employers can browse. You can also use your blog as a jumping-off point for pitching your ideas to online publications.
You don’t have to be the biggest name in data science to land a speaking engagement. You can join a local meetup and offer to run one of the sessions. If you have a recording of the session, this is a wonderful way to showcase your spoken skills.
You can also turn one of your blog posts into a slide presentation or video yourself talking about the subject. If you’re uncomfortable speaking in front of others, you might consider joining a group like Toastmasters (or your local equivalent) to get out of your shell.
Use these speaking engagements to share your knowledge and to gain valuable contributions for your portfolio. Employers can learn more about data science and about you at the same time.
Augment your resume
Resumes aren’t dead, not by a long shot. However, in the new hiring climate, you’ll need a dynamic element to your resume to stand out. A well-rounded portfolio showcasing your accomplishments and soft skills is just what your employer needs to see you as an accomplished Data Scientist.
Finding your next job after building your data science portfolio
After working on your data science portfolio, there are a number of ways you can start preparing for a job.
Find jobs with the Ai+ Careers platform
There are plenty of job sites out there, but our own Ai+ Careers site is designed specifically for data science and artificial intelligence professionals. Featuring automatic job matching, job assessments, and advanced career searches, this is your go-to tool for finding your next job.
Scout the ODSC jobs board daily
We frequently add new jobs to our data science jobs board – bookmark it and check daily for new in-person or remote data science jobs.
Learn new skills with Ai+ Training
On the Ai+ Training platform, you gain access to all of the most important skills that employers are looking for when looking for data scientists. Our on-demand and live courses will dive into anything from machine learning fundamentals to how to use specific tools for data visualizations. Upcoming live training sessions include:
- Introduction to Python Programming: Oct. 12th | Matt Harrison | Python & Data Science Corporate Trainer | MetaSnake
- Getting Started with Practical MLOps: Oct. 21st | Noah Gift | Founder/Adjunct Professor | Pragmatic A.I. Labs/Duke MIDS & Northwestern Graduate Data Science & AI
- Gradient Boosting: Oct. 28th | Brian Lucena | Principal | Numeristical
- Web Scraping & Social Media Mining for Text Analysis & NLP: Dec. 14th | Minerva Singh, PhD | Deep Learning and Machine Learning Instructor