Don’t learn to be a Data Scientist

LearningIt’s my custom to learn continuously – I keep a technical book, course, or class going at all times, and the latest I completed was a course called “Learning How to Learn” on Coursera. (highly recommended, by the way).

The instructors covered an interesting topic in that course – that you should not focus on the result of what you’re learning. In other words, don’t focus on the degree, or the certification, completing the class, or in fact even learning the topic. Instead, they say to focus on the process of learning – in essence, switch your goal from “I want to finish this class” to “I want to spend 30 minutes this morning learning this material.”

Why do this? Actually, it’s great advice. Whenever I teach Data Science classes, I see some of my students get really enthused about the material (I do too). They are eager to find any source they can to jump in and start attacking the subject. But shortly, they realize just how much math, logic, and other skills are needed – and it’s a lot. So they get a little discouraged, and inevitably I get the question “Is there a way to learn this faster?” In our iPhone-attention-span world, we want a quick way to get to the goal.

But there isn’t one. Math is detailed, and there’s a lot of it to learn. There’s a lot of statistical knowledge you need. And the domain experience to do a good job can take years.

So if you focus on the goal alone – becoming a Data Scientist – you can get discouraged. It might be overwhelming. But if you focus on spending 30 minutes every morning (or evening, if that’s your thing), religiously, every weekday, that’s a goal you can hit. And when you don’t, you just make that goal for the next morning. And you hit that one. And the next one. And the next one.

That doesn’t mean you shouldn’t have an overall goal of being a Data Scientist, or getting that degree, or learning that subject. That’s why you do the training to begin with. But after you carefully consider what you want to do in life, then make that decision and put it to the side. That’s no longer the goal – it’s the decision. Daily learning becomes the goal.

Also – keep in mind there are levels in Data Science. The bulk of the work isn’t the math and algorithms. The bulk of the work is in data cleanup and preparation. So while you’re doing your daily learning, focus on those tasks first – you’ll become usable in a Data Science team quicker and be able to jump in and add value right away. And that gets you closer to the bigger goal.




5 thoughts on “Don’t learn to be a Data Scientist

  1. I noticed as a student a few years ago that many people were doing just enough to pass or doing it for the good grades and not so much on learning. I never liked this and I took courses with the intent of learning.

    Currently, I am working on entering the data science field in Canada (in Toronto or Waterloo) from the mathematics and statistics route.

    As much as I want to get there ASAP, I realized that my programming skills in R (and Python) need more work as programming is not emphasized enough in math and statistics programs (I think). I am relearning some aspects of R along with data cleaning, data manipulation and data visualization methods (pretty much the stuff I was not taught in school) and so on.

    Eventually I would want to be comfortable with Machine Learning and other algorithms.


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