Career In Tech
6 Things Grad Schools Fail to Teach Me about Data Science
First off, here is a big personal update: in March 2022, I officially graduated from my Ph.D. program and earned the doctoral title. So, please address me as Dr. Ye since today (kidding).
While looking back at the long journey, surviving grad school takes more than hard work and dedication; it also requires a strong stomach for the ups and downs as you grind through the process. Being able to cross the finish line (phinally) was an emotional moment in my life, and there is no shame in admitting that I shed a few drops of tears.
Besides, May 2022 marks the 6-month anniversary of working in tech. There are valuable learnings and lessons that I’d like to share with the broader community. Well, why don’t we kill two birds with one stone? So, I dedicate this post to something special.
Recently, I was asked for my perspective on Data Science (DS) education and if a post-graduate degree in Statistics is a career booster or dragger. Here is my two-cent: a graduate degree in Stats is definitely rewarding and valuable to one’s tech career in DS and beyond. In my daily routine as a Data Scientist (Statistician), I’m using and re-using all the knowledge learned from my graduate training. However, there are things that I wish grad school should have trained me more extensively.
Let’s look at some personal struggles:
- Why was it so difficult to land interview opportunities even with multiple post-graduate degrees?
- Why didn’t I pick up the tech stack (e.g., cloud computing) used in the industry early on?
- Why did I feel unguided and unprepared in the interview process?
Certainly, it is unrealistic to expect academic programs should address all of these concerns. After all, grad programs are academic in nature and not designed specifically for the job market. They are not DS boot camps that train students for a specific type of occupation. Instead, the original intention of a Statistics grad program is to introduce fundamental statistical…