During my time in data science and machine learning, I have met many employers and interviewers. So here in this article, I would like to share how to prepare your data science resume and even interviews for a Data Science Job role. I can’t promise any results, but I hope it can help those who are passionate about data science. The advice given is really for those who are passionate about it because it takes a lot of effort. So let’s see what we should include in a Data Science Resume to leave a good impact.
Include Projects in Data Science Resume
The title is explicit, but let me take this opportunity to explain it further. I have often been asked: Can we use our university projects as part of the Data Science Resume. Here are some considerations I have if you want to present your academic projects.
First, most of these academic projects are guided and team-oriented. It is very difficult to differentiate which part of the project is carried out by whom.
The best project to present is outside the program, in their spare time, because it shows that the person is passionate about data science and ready to devote his spare time to it. An interviewer can also ask the interviewee to explain everything that is done in the project.
Have keywords in your Data Science Resume (such as machine learning models used, model training process, etc.) used to explain the project, but be prepared to explain those keywords, especially when it comes to a technical keyword. An interviewer tends to ask respondents to explain these keywords and the explanation should be at a level that the layman can understand. Well, if you can not explain it to the layman, it means that you still do not fully understand it.
Show that you can work as a team
If one wants to work in the field of data science and machine learning, being able to work as a team is very important, whether in a leadership role or as a team player. It is therefore important to present all team projects in the Data Science Resume as well as the impact obtained, preferably by quantifying the impact so that the interviewer can make a good mental impression.
The impact will allow the interviewer to know whether the interviewee can work as a team or not. The interviewer also tends to ask the interviewee to share more of his or her experience in at least one project to see if he or she can work as a team.
Include programming skills in Data Science Resume
Whenever possible, introduce all written codes, especially if the codes are written for data science projects. Otherwise, other programming languages are welcome, it does not necessarily need the common languages used in data science.
Make sure it is well documented. Well documented, which means there is a good description of what the code does, why the codes should be written this way, why the code is implemented, etc. the main goal is for the interviewer to understand the reflection process by writing codes and drawing ideas from the project in your Data Science Resume.
I hope the shared points will help you build a more engaging portfolio and data science resume to your potential employers and also be well prepared for any upcoming interview questions.
I hope you liked this article on how to prepare a Data Science Resume. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Data Science and Machine Learning.