5 steps to actually learn data science

Data science is a broad and varied field, and hence the path to becoming a unicorn is full of darkness. To light up your path and guide you to become one, here are 5 simple steps to be followed.

It’s an exciting time for data science. The field is new, but growing quickly. There’s huge demand for data scientists – average compensation in SF is well north of 100 thousand dollars a year. Where there’s money, there are also people trying to earn it. The data science skills gap means that many people are learning or trying to learn data science.

The first step to learning data science is usually asking “how do I learn data science?”. The response to this question tends to be a long list of courses to take and books to read, starting with linear algebra or statistics. I went through this myself a few years ago when I was learning. I had no programming background, but knew that I wanted to work with data.

I can’t fully explain how immensely unmotivating it is to be given a huge list of resources without any context. It’s akin to a teacher handing you a stack of textbooks and saying “read all of these”. I struggled with this approach when I was in school. If I had started learning data science this way, I never would have kept going.

Some people learn best with a list of books, but I learn best by building and trying things. I learn when I’m motivated, and when I know why I’m learning something. Best of all, when you learn this way, you come out with immediately useful skills. From my conversations with new learners over the years, I know many share these views.

That’s why I don’t think your first goal should be to learn linear algebra or statistics. If you want to learn data science, your first goal should be to learn to love data. Interested in finding out how? Read on to see how to actually learn data science.

Fig.1 An example of the visualizations you can make with data science (via The Economist)

Nobody ever talks about motivation in learning. Data science is a broad and fuzzy field, which makes it hard to learn. Really hard. Without motivation, you’ll end up stopping halfway through and believing you can’t do it, when the fault isn’t with you – it’s with the teaching.

You need something that will motivate you to keep learning, even when it’s midnight, formulas are starting to look blurry, and you’re wondering if this will be the night that neural networks finally make sense.

You need something that will make you find the linkages between statistics, linear algebra, and neural networks. Something that will prevent you from struggling with the “what do I learn next?” question.

My entry point to data science was predicting the stock market, although I didn’t know it at the time. Some of the first programs I coded to predict the stock market involved almost no statistics. But I knew they weren’t performing well, so I worked day and night to make them better.

I was obsessed with improving the performance of my programs. I was obsessed with the stock market. I was learning to love data. And because I was learning to love data, I was motivated to learn anything I needed to make my programs better.

Not everyone is obsessed with predicting the stock market, I know. But it’s important to find that thing that make you want to learn.

It can be figuring out new and interesting things about your city, mapping all the devices on the internet, finding the real positions NBA players play, mapping refugees by year, or anything else. The great thing about data science is that there are infinite interesting things to work on – it’s all about asking questions and finding a way to get answers.

Take control of your learning by tailoring it to what you want to do, not the other way around.

Fig. 2 A map of all the devices on the internet