It’s hard to overestimate how I adore StackOverflow. One of the recent blog posts on StackOverflow.blog is “What you need to know to start a career as a data scientist” by Julia Silge. Here are my reservations about that post:
1. It’s not that simple (part 1)
You might have seen my post “Don’t study data science as a career move; you’ll waste your time!“. Becoming a good data scientist is much more than making a decision and “studying it”.
2. Universal truths mean nothing
The first section in the original post is called “You’ll learn new things”. This is a universal truth. If you don’t “learn new things” every day, your professional career is stalling. Taken from the word of classification models, telling a universal truth has a very high sensitivity but very low specificity. In other words, it’s a useless waste of ink.
3. Not for developers only
The first section starts as follows: “When transitioning from a role as a developer to a position focused on data, …”. Most of the data scientists I know were never developers. I, for example, started as a pharmacist, computational chemist, and bioinformatician. I know several physicists, a historian and a math teacher who are now successful data scientists.
4. SQL skills are overrated
Another quote from the post: “Strong SQL skills are table stakes for data scientists and data engineers”. The thing is that in many cases, we use SQL mostly to retrieve data. Most of the “data scienc-y” work requires analytical tools and the flexibility that are not available in most of the SQL environments. Good familiarity with industry-standard tools and libraries are more important than knowing SQL. Statistics is way more important than knowing SQL. Julia Silge did indeed mention the tools (numpy/R) but didn’t emphasize them enough.
5. Communication importance is hard to overestimate
Again, quoting the post:
The ability to communicate effectively with people from diverse backgrounds is important.
Yes, Yes, and one thousand times Yes. Effective communication is a non-trivial task that is often overlooked by many professionals. Some people are born natural communicators. Some, like me, are not. If there’s one book that you can afford buying to improve your communication skills, I recommend buying “Trees, maps and theorems” by Jean-luc Doumont. This is a small, very expensive book that changed the way I communicate in my professional life.
6. It’s not that simple (part 2)
After giving some very general tips, Julia proceeds to suggest her readers checking out the data science jobs at StackOverflow Jobs site. The impression that’s made is that becoming a data scientist is a relatively simple task. It is not. At the bare minimum, I would mention several educational options that are designed for people trying to become data scientists. One such an option is Thinkful (I’m a mentor at Thinkful). Udacity and Coursera both have data science programs too. The point is that to become a data scientist, you have to study a lot. You might notice a potential contradiction between point 1 above and this paragraph. A short explanation is that becoming a data scientist takes a lot of time and effort. The post “Teach Yourself Programming in Ten Years” which was written in 2001 about programming is relevant in 2017 about data science.
Featured image is based on a photo by Jase Ess on Unsplash