Data Science: Boot Camp Supps
Supplemental Materials to help get through your Data Science Bootcamp!
Participating in a bootcamp, especially a full-time one, is a fast learning experience. Given the time frame and breadth of material to cover, it was hard to imagine that I would really get everything I needed to succeed from one source. Knowing about how you learn best is really helpful going into the bootcamp — you can plan for what else you might need. I tend to learn well by reading and doing, but I find after an hour or so of consuming material I need to switch to either a different format or practice on my own. Here I’d like to share the tools and resources I’ve used going through a full-time, online bootcamp and why I think they’re particularly useful. These would also be great resources for self-teaching.
RealPython
I started using RealPython articles to learn long before I enrolled in a bootcamp. When I was just beginning to use Python to automate some of my report pulling/cleaning I often visited the site. RealPython has many informative, in-depth articles about how and which tools to use, and information about why you might choose that particular tool. There is information, in most cases, for all common operating systems. At one point I decided to enroll in the paid membership to access a video course or two. I’ve found that I use it often enough that I feel like the subscription adds an appropriate level of value to my learning experience for the price. When I started the bootcamp and felt unsteady using Git, having a digestible, clearly explained video course on it was helpful. RealPython also has more information about quality coding in Python, which is not something that the bootcamp I participated in focused on.
Books
An Introduction to Statistical Learning
I’ve worked through this book, doing the exercises and reading, approaching the end of my bootcamp to solidify by knowledge. Working through a different type of material has been a helpful way to build skills and confidence.
A Programmer’s Introduction to Mathematics
Jeremy Kun’s blog is also an excellent resource — I chose to buy the book because I wanted to support the work and because I like to keep learning even when I need to take a break from a screen.
Dr. Goel “[looks] at public policy through the lens of computer science, bringing a computational perspective to a diverse range of contemporary social issues”. There are many resources for learning about the importance of developing equitable algorithms, but I’ve particularly enjoyed reading some of Goel’s work as a part of my learning experience.
Nate Silver’s book helps contextualize the power of statistical learning. He writes about topics you interact with frequently: weather prediction, sports, poker, stocks, and more. He explores the historical background of many of these long-running attempts at prediction and modeling.
Youtube
Statistics and Machine Learning — clearly explained!
Beautiful, visual, clearly explained mathematics concepts.
Other Supplies
Index cards + pencil/pen
If you’re anything like me, jumping into a combination of programming/statistics/linear algebra/calculus feels like starting a creaky, un-oiled machine in the basement of your brain. When going through labs, write down definitions and notes — anything you want to be able to quickly recall, on your cards. Stick your stack of notecards somewhere handy and flip through them OFTEN. Bootcamp concepts are quick fire — you’ll need a way to keep ALL of that information in your brain, not just what you’re studying this week.
A support system
What you’re doing is hard. A career change is not easy. Imposter syndrome is real. Having a support system outside of your classmates is essential!
Best of luck with your learning journey!