Grad school takeaways

While it is not uncommon to see data scientist roles requiring an advanced degree, I would say graduate school is primarily a means to an end (and it is not the only, nor should it be the only). The main reasons behind me pursuing a further education are to: (i) give myself a challenge and (ii) add variety to my working life - hence my decision to do it on a part-time basis. And why data science? It seems like a natural progression from my undergraduate studies in Statistics and the courses seemed interestingly different with more relevance to industry challenges.

Here's my two cents' worth on grad school:

- We don't know what we don't know and grad school helps us to discover what we don't know. There are instances of knowing what we don't know (eg. Python skills) and wanting to fill that gap through grad school, but we are often unaware of what's out there until the subject is thrown in our face. I used the phrase"helps us to discover" instead of "teaches us" because most of the time the method/ approach to solving a particular problem is covered briefly and eventually we have to figure out on our own through research and trial-and-error. No more spoon-feeding, unlike undergraduate days, which makes sense because that reflects the reality of working environment. (But some might then question what they are paying so much money for.)

- We're often advised to work in teams with complementary skills. It's not an exaggeration to say such teams make assignments (and our own lives) a lot easier and less stressful. Under tight project timelines, the beauty of specialization is exemplified. However, most of the learning takes place through making mistakes during those trial-and-error moments. Hence, in order not to eliminate the opportunity to develop our weaker areas, we can always work the code developed by our teammates on other similar context when we have the time. We all learn from one another.

For some who might be interested in the technical details, I've also outlined the various tasks we completed in the five core modules of the Master of Science in Business Analytics program at National University of Singapore (NUS) during my term AY14/15 and AY15/16.