4 Skills You Need to Become a Data Scientist
…and how you can start improving those skills
Interested in becoming a data scientist? Here are some critical data science skills that you will need in order to make the career change. For each data science skill listed, there is also corresponding advice and resources on how to improve that specific skill. This is by no means an exhausted list and instead is meant to be an overview of what you will need in order to succeed as a data scientist.
1. Problem solving intuition
Being good at problem solving is very important to being a good data scientist. As a practicing data scientist, you don’t just need to know how to solve a problem that’s defined for you, but also how to find and define those problems in the first place. It starts with becoming comfortable with not knowing the exact steps you will need to take to solve a problem.
There is no one right way to learn problem solving intuition. Personally, learning how to code has greatly expanded my problem solving skills (which is #3!). In the meantime, here are some excellent TED talks that I would recommend watching on problem solving.
2. Statistical knowledge
When working in data science, the math and statistics applied can often be obscured by the fact that you’re just writing code or using functions. The better you understand that underlying process, the better you’ll be at using it. For example, you must be able to understand when variations in the data are statistically significant so that you can make bigger assumptions and conclusions about what’s going on. There is so much to learn in this realm and the more knowledge you have, the more accurate conclusions you will be able to draw from a given dataset.
Want to start boosting your stat knowledge asap? Check out Khan Academy’s free Statistics and probability course.
3. Programming in an analytic language (R or Python)
Knowing a programming language is essential in order to become a data scientist. Programming allows you to take vasts amounts of data and process them quickly in a meaningful way. You’ll also be able to use programming to do things like scrape websites for data or use APIs. Right now some of the most popular languages for data science analytics include Python or R.
New to programming? Try out Codeacademy’s Python course (also free)
4. Curiosity (keep asking why)
Not only will curiosity keep you driven to continue your learning in the long run, but it will also help you know what questions to ask when you are diving into a new set of data. Your first answer is rarely the right one. If you keep diving deeper you may find things that surprise you, or change your whole understanding of the problem!
Similar to problem solving skills, there is no one way to increase your curiosity. Something I’ve found works for me is setting aside an hour a day for “unstructured time”, before or after the typical tasks that make up my day. Giving yourself space for learning or projects outside of your day to day work is a great way to keep yourself curious and inspired.
Let me know if there is something else you think is a critical data science skill! Also, if you are ready to get fully immersed in learning these skills and more, check out Thinkful’s Data Science bootcamp. We use a combination of 1-on-1 mentorship, project-based curriculum, and career services to help you make the career transition and become a data scientist.
Originally published at www.thinkful.com on May 9, 2017.