How to become a Data Scientist?

Recently the SlideRule Team bring me an email with the following 5 tips of how to become a Data Scientist:

“I’m an X major who wants to be a Data Scientist. Where do I start?”

In this email, we’re sharing 5 important tips that will help you get on the path to Data Science excellence (and some further reading on each topic). If you have your eyes set on the Data Science job market, these tips are crucial.

  1. Develop an area of technical analytic expertise: Start with a solid foundation in statistics. Once you’ve built that, learning Advanced Statistics, Machine Learning, or Natural Language Processing could come in handy. If you are still in school, take courses in these subjects. If not, develop expertise in any one of these areas, and try to be conversant in a few others.
    More reading – A Neural Network in 11 lines of Python (github.io)A Visual Introduction to Machine Learning (r2d3.us)
  1. Build an affinity for code: No two ways here. Hacking skills might be even more important than formal systems development here. As a entry-level Data Scientist, a lot of your work will be to take lousy data and put it in a form that can be analysed. And it will be different with each data set you work on. Learning Python or R will serve you well.
    More reading – Comparing Python and R for Data Science
  1. Learn to tell a story (with data):  First, learn basic statistics. Second, be able to express your brilliant analysis in a way that normal people can understand. Your clients and colleagues won’t always understand what terms like “p-value” mean. You need to explain your results well, why they are significant and why someone should trust them in a way that laymen understand. Visualization techniques can be helpful here.
    More reading: How to Tell a Powerful Story with Data Visualization
  1. Get a mentor: A hands-on, project-based approach is best when you’re learning the ropes. You will make some mistakes. Having someone with experience to talk to, review your work, and keep you accountable – it’s easy to stay on track.
    More reading: Nate Silver on Finding a Mentor, Teaching Yourself Statistics, and Not Settling in Your Career
  1. Build a strong portfolio: Building projects is not just the best way to learn, it’s also a great way to showcase the skills you’ve acquired. The best companies will want you to demonstrate that you can work through a data problem end-to-end: from data gathering and cleaning, to analysis and clearly communicating your findings. An effective way to start building portfolio is to enroll in some Data Science competitions.
    More reading: Tips for Data Science Competitions

Was this email helpful? Got questions? Hit reply and let us know!

Talk soon!

The SlideRule Team

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