Becoming a self-taught data scientist can be daunting, but it can pay great dividends and be fairly cheap. Our suggestion is to treat it more as a hobby rather than an academic assignment. You’ll start off slow, but gain skills along the way, and there won’t be pressure to get everything done perfectly. Furthermore, it’s coding, and if you do mess up, it becomes clear pretty quickly.
Here is a list of different skills needed to be successful in data science:
- Python (for data scientists and engineers)
- Spark (for data scientists and engineers)
- Hadoop (for data engineers)
- SQL (for data scientists and engineers)
- NoSQL (for data engineers)
- Machine Learning (for data scientists)
- Deep Learning (for data scientists)
- Natural Language Processing (for data scientists)
- R (for data scientists)
- Tableau (for data scientists and other analytics professionals)
What you may have noticed is that some programs overlap and are used consistently in certain industries. R is a stats friendly program and used a lot in metrics. Python is one of the easiest and widely used programs but still has its limits.
I would recommend using Udemy, DataCamp, and LinkedIn Learning as platforms to buy and build your coursework. I specifically like DataCamp because it’s all on the same platform without having to go into the command prompt or downloading zip files as you do on other platforms.
Lastly, YouTube is a super learning friendly library of knowledge when it comes to programming. As you get more advanced, there are online communities to help troubleshoot issues and often times your issue will already have arisen and been solved by other programmers.
So, have fun and start coding away! Jump right in, float, swim, sink, or a mixture of it all, but realize at the end of the day you are gaining skills that aren’t available to most other business professionals.