Data Science is new and means different things to different people. And most of all, you’re worried that since you are learning skills on your own time at home, you may not be learning the right things. So, when you construct your data science portfolio, it may be all wrong.
Your Data Science Portfolio should work for you
Your dream is to have created a portfolio that gets you a job offers without you needing to reach out to employers. You want recruiters and employers reach out to you cold to offer you employment opportunities based on your portfolio. You’d like your future employer to already know you by reputation because they’ve already seen your portfolio.
You are going to build a Data Science Portfolio
The question is – where to start. How can you make sure that it’ll look great before, during, and after the interview? How can you make sure that your portfolio works entirely for you?
Creating a Data Science Portfolio
The “magnifying glass fire starting” approach to building a data science portfolio. You can use a magnifying glass to start a fire because it concentrates the sunlight going through it to such an intense degree that the concentration of heat can reach incredibly high temperatures. You want to do the same thing with your portfolio – make it so concentrated and intense that your future employers will salivate at the thought of having you join their team.
Here are the 10 steps to create a data science portfolio that will get you hired:
- Forget about boiling the ocean
- Take a hyper-targeted “magnifying glass fire starting” approach
- Find 5-10 data science jobs you’d take if offered to you
- Figure out what skill sets/job responsibilities you would have
- Find the common ones (NLP, recommendation, classification, etc.)
- Figure out the toolsets that the jobs require
- Find common tools (R, Python, Scala, Hadoop, etc.)
- If it’s not a tool or tools you know, then learn them as part of the portfolio work
- Do 3 projects that cover the common job responsibilities for the jobs you are interested in using the common tools for those jobs.
- Do a structured write up for each of the three projects.
Don’t overthink it. After having looked at the data of the types of data science jobs you want to do, you should have an okay idea of potential projects. Rather than worrying too much about it, start the first project. Then you can re-evaluate what you will do for the second one. Then after the second project, you can then re-evaluate what you do for the third project. As you can imagine, you’ll have clearer and clearer thoughts as you go through these projects from start to finish.