My post-bootcamp learning plan
Building my own bridge from student to professional
Earlier this year I completed Flatiron School’s full-time online data science bootcamp. The program took about 40 hours per week for 5 months, and it covered pretty much everything I expected from a data science bootcamp (plus some things I hadn’t even heard of before I started!). It’s impossible, of course, for any bootcamp to cover everything a data scientist needs to know, and that’s not really the point. Flatiron School’s stated objective for the bootcamp is to introduce students to a wide range of material and teach them how to learn new skills on their own.
After graduating and while starting my job search, I set out to fill some gaps in my preparation. Reading job ads taught me a lot about what employers are looking for and what specific skills were required for the sorts of jobs that appealed to me most. In this post I’ll share some of the resources I have used post-bootcamp to broaden and deepen my data science knowledge.
The bootcamp curriculum
Flatiron School is always updating its data science curriculum, but just for context, here is a general outline of what we covered:
- General Python programming, with particular focus on NumPy, Pandas, and Matplotlib
