There’s no doubt about it: data scientists are in high demand. As of 2020, the average data scientist in the US makes over $113,000 a year, and data scientists in San Francisco make over $140,000. Learn data science and you could find yourself working in this promising, well-compensated field.
But even if you’re not interested in becoming a data scientist, learning data skills and improving your data literacy can pay big dividends in your current career. Employees who have data skills and can help their companies become more data driven are in demand across almost any industry.
How to Learn Data Science
So how do you start to 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 demotivating 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. From my conversations with new learners over the years, I know many other learners feel the same way. There’s some science behind this, too.
Plus, there’s another big advantage to this approach. When you learn this way, you come out with immediately useful skills.
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 or just pick up some data science skills, 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.
An example of a data visualization you can make with data science (via The Economist).
#data-science #data-analysis #machine-learning #artificial-intelligence #deep-learning
