How to Prepare Your Data Science Resume and Portfolio

In the past few years, I have met up with a lot of employers and conducted interviews for training program. Through the conversations and interviews, and together with the end results, I thought I will share more on how to prepare your resume and even the interviews for a data science role. Most of the tips are for people who want to enter into the data science profession with a “green” background. I cannot promise results but I hope it can help the passionate get into data science. The tips given are really for those who  are passionate in it as it requires a lot of effort.

Projects

Get a project portfolio! One FAQ I get when I asked someone to get a project portfolio, “Can I use my school project as part of the portfolio?” Now here are some considerations I have if one were to showcase their school projects.

Firstly, most of these school projects are guided (hopefully) and are usually group project (so its easier to mark and can be graded quickly). Its very challenging to differentiate which part of the project is done by who. What I can only infer is that, based on the results of the project, whether the team is functional or dysfunctional. Potential employers will have doubts about your capabilities even if the project is well done. Its not conclusive evidence of your capabilities.

The best project to showcase is done outside of the curriculum, during one’s free time because it shows that the person is passionate in data science and willing to spend their free time on it. I can also attribute whatever that is done in the project to the interviewee. But that is after I have asked a few more questions on the project to ascertain it.

Do have keywords (such as the machine learning models used, the model training process etc) used to explain the project but be prepared to explain these keywords especially when it is a technical interview. I tend to get interviewees explain the keywords they used and the explanation has to be at a level that the layman can understand. Well, if one cannot explain it to the layman, it means one still does not understand it completely right?

Team Work

If one is to work in the data science or even AI, being able to work in a team matters tremendously, regardless of being in a leadership or team player role. So it is important to showcase any team projects and also the impact achieved, preferably quantify the impact, so the interviewer can build a good mental impression. The impact will give me some information on whether the interviewee can work in a team or not. I also tend to ask the interviewee to share more about their experience in at least one project, so as to ascertain if he/she can work in a team.

Maths & Statistics Background

I like a good grasp on the level of mathematics and statistics that the interviewee has. It can be inferred from the module grades, projects and tools that they used.

Module grades does help to ascertain the level. I usually look at the whole portfolio of mathematical modules that were taken as a whole to ascertain the  level of maths and stats background. I do give chance for those that  have mediocre grades but I will definitely ask the interviewee why the  mediocrity. I do ask the interviewee about what they like and do not like about maths and stats to determine if he/she can work with the mathematics required in data science and AI.

The projects and tools does help to infer the maths background. This is  seen through the machine learning models they have used, how they  implement it and why they implement it in a particular manner. I may ask what were the challenges they faced during these projects, the reason a particular solution was chosen and as much as possible  relate to the mathematics behind.

Programming

Where possible, do showcase any codes written, especially if the codes are written for data science projects. Otherwise, other languages are welcomed, not necessary must be those common languages used in data science (R, Python, Scala).

Make sure it is well-documented. Well-documented meaning there is a good description on what the code is doing, why the codes need to be written  in such a way, why the code is implemented etc. The main objective is for the interviewer to understand the thought process interviewee have gone through in writing codes and deriving insights from the project (Here is my blog post on how to improve it). Documentation is very important in order for data science to be reproducible, interpretive and accountable. Showcasing  the thought process is a very important consideration for the  interviewer to determine how much autonomy can be given to the new hire  to get meaningful results from the project.

Thus a well-documented code is very important, as it can indicate the level  of knowledge, skills and thinking of the potential hire.

Conclusion

The  above points are what I have gathered interviewing for training program  and talking to numerous employers. I hope the points shared will help you to construct a more “attractive” portfolio and resume to your  potential employers and also be well-prepared for possible interview  questions to come.

I would also like to recommend the following article, “How to Construct a Data Science Portfolio from Scratch”.

Thanks for your kind support in reading till here. Do consider subscribing to my newsletter.  I wish you all the best in your Data Science journey! If the article is  useful, do share with your friends and consider giving me a shoutout at  LinkedIn or Twitter. :)