How To Learn Data Science

Learning data science can find you 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.

So how do you start to learn data science?

There are different ways to learn data science, go to university, follow a bachelor or master in data science, get into a Bootcamp program, or learn it by yourself using a long list of books. Nowadays a lot of material is available on the internet, often for free, to learn the skills necessary for Data Science. But it can be immensely demotivating 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.” Some people learn best with a list of books, some others learn best by building and trying things. I learn when I’m motivated, and when I know why I’m learning something.

Studies have shown that most people learn best by doing. Plus, there’s a big advantage to this approach. When you learn this way, you come out with immediately useful skills. With this, I’ll advice that the first approach to learning data science is to first love data.

People rarely talk about how motivation is so important and of immense value in learning. Data science is a broad and fuzzy field, which makes it hard to learn. You need something that will help 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. You need motivation. Not in the form of an inspiring quote, but in the form of a passion project you can use to drive your learning.

I am obsessed with improving the performance of programs/projects I am involved in. Mother and Child healthcare was my entry point. I believe that many maternal and neonatal deaths can be prevented if we can have a pathway to their healthcare processes. That can be possible if we follow up on every of their health practices and clinical visits, take records of them all and measure them against parameters we are looking out for. And as I worked, I was learning to love data. Because I was learning to love data, I was motivated to learn anything I needed to make my programs/projects better.

Bottom line, find that thing that makes you want to learn. Take control of your learning by tailoring it to what you want to do.

Another best way to learn is to work on projects. By working on projects, you gain skills that are immediately applicable and useful, because real-world data scientists have to see data science projects through from start to finish, and most of that work is in fundamentals like cleaning and managing the data.

(Working on projects as you study also gives you nice way to build a portfolio. This will be tremendously valuable when you’re ready to start applying for jobs).

So how can you find a good project? One technique to start projects is to find a data set you like. Try to answer an interesting question about it. Rinse and repeat. Another technique is to break complex problems into small steps and solve, one after another. Learning without application is easy to forget. More important, if you’re not actively applying what you learn, your studies won’t prepare you to do actual data science work.

How can you know that you’re learning if you don’t communicate your results with other people?

Communicating your insights is difference between being an okay data scientist and a great data scientist. Data analysis is naturally accepted and acted upon only if you are able to convince people on what you have found. That means you have to learn to communicate.

Part of communicating insights is understanding the topic and theory — you’ll never be able to explain to others something that you don’t understand yourself. Another part is understanding how to clearly organize your results. The final piece is being able to explain your analysis clearly.

It’s amazing how much you can learn from working with others. In data science, teamwork can also be very important in a job setting. Data scientists often work as part of a team, and lone data scientists at smaller companies will typically work together with other teams at their company to solve specific problems. It’s not unusual for a data scientist to move from team to team as they work on answering data questions for different arms of the company, so being able to collaborate may be more important for data scientists than almost anyone else!

Learning from peers can make learning data science more interesting because you get different opinions and insights that can influence your data analysis to give or predict appropriate outcome. You never can tell how much needed knowledge a colleague can bring to the table if you don’t collaborate with them.

Finally, are you completely comfortable with the project you’re working on? Was the last time you used a new concept a week ago? It’s time to step up and work on something more difficult.

  • Increase your data set.
  • See if you can make your algorithm faster.
  • How would you scale your algorithm to multiple processors? Can you do it?
  • Understand the theory of the algorithm you’re using better. Does this change your assumptions?
  • Try to teach a novice to do the same things you’re doing now. This one is a really underrated challenge, it will give you a deeper understanding of the topic and also improve your communication and explanation skills.

In conclusion

There’s no doubt about it: data scientists are in high demand. But more than getting the certification, coming to love data science can make a huge difference and you can surmount the daunting mountain of data science, making it a walk in the park for you.


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