No, really. Someone actually said that. Probably the same type of person who decided the featured image of this post should be tagged as data science.
Data science has become a melting pot for over-educated millennials from STEM fields (yours truly included) because companies are finally realizing that quantitative evidence is a good way to make decisions.
There are a wide variety of projects that data scientists actually work on, far too many to list in a 600 word blog post. From optimizing marketing campaigns to building AI-powered chat bots, data scientists live at the intersection between software development and applied math.
What’s it take to be one? It’s pretty simple: be a decent software developer, a great statistician, and thoroughly commit yourself to backing up everything you say with quantitative evidence.
If you have a graduate degree in a STEM field and can handle yourself around common scripting languages, you’re off to a great start. If you’ve never coded in your life, you’re going to have a harder time. There are a wide variety of [expensive] data science boot camps out there to teach you the basic skills, but if you’re a self learner, here are some resources to get you started on the right foot:
Brush up on coding on HackerRank
This is one of the best places to learn and improve your coding skills. HackerRank’s format features larger tutorials as well as individual challenges that only take 5 – 10 minutes with your language of choice. Some firms actually use HackerRank’s format for the technical portion of their interview, so it’s well worth it to at least get familiar with the process.
If you’re used to taking a 15 minute social media break during the day, consider doing a few HackerRank challenges instead.
Learn through data science notebooks
iPython notebooks are a common format for exploring data science projects. Once you have your basic coding skills down, these are a great way to get exposed to common approaches in the field:
Get used to containerization
Data science models often rely on uncommon mathematical libraries. Many firms are moving towards containerization to make it easier to build and deploy data science models internally and for clients. Being familiar with the basics of Docker will help you navigate the conversations around “microservices” and “containerization” that you will inevitably run into: