| a degree in computer science is really inefficient if you want to be a data scientist. imagine a 10 class CIS masters: with graduation restrictions you might be able to take 3 classes that directly relate to data science. good luck in your job interviews if you took them first, as you spent 20 hours a week on homework to fill requirements you will never use. now take a statistics major, every class is relevant, and you can still take machine learning in your electives. win win. I came to this conclusion after I noticed more of my classmates in the mba program (wharton) as data scientists than people in computer science who took machine learning. in fact, _all_ of the CIS majors in machine learning who really wanted to be a data scientist ended up as engineers. so then I started doing a small search on linkedin, only looking at the big tech company data scientists. selection biases aside, out of 12 profiles: 5 statistics majors, 5 business, 1 biophysics, 1 IT major. I have also done some looking into interview questions via glass door, and you get grilled on statistics questions. this matches my one interview with uber in 2016. I only got asked 2 ML questions: what is random about a random forest, and in KNN, what happens to bias & variance as K goes to 1 if you want to be a data scientist, you need to learn stats really well or getting past the interview process is going to be very difficult. |