Pursuing Data Science Part II: Helpful Articles & Online Prep Courses
In Part I, you learned about the difference between data analytics & data science, saw how Meetups can help you decide if the career move is right for you, and even received a suggested LinkedIn message to send to current students and alumni to get the real inside scoop on a program you’re interested in.
Now that you’re set on making the switch, I’m sharing articles and courses that helped me prepare for the program — which I mostly did at local coffee shops or one of many amazing Denver breweries! (as pictured above 😉 )
Articles
Here are some articles and books that were helpful for me and a brief description why.
Starting with something that was helpful when sharing the news with family who are less technologically-savvy. Even when I was an account manager in advertising, my mom had a hard time telling people what I did. I found this video to send to her and my family when I told them I’d be making the switch into data science. Can she tell people what I do now? Probably not. But she’s happy that I’m happy.
These books and two articles were helpful as I started to reshape my thoughts and feelings around some narratives I didn’t know I thought to be true — “Girls aren’t good at math” and “Girls don’t code.” The first three are written by women who did not come from particularly strong backgrounds in what their focus is now. I enjoyed their stories and the book does a great job of providing you tools you can apply when solving math problems.
- Learning Fluency (20 min read)
- How I Rewired My Brain to Become Fluent in Math
- A Mind For Numbers: How to Excel at Math and Science (Even If You Flunked Algebra) by Barbara Oakley
- An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
- Regarded as a “valuable resource for practicing data science” and for very good reason. Many of our daily readings came from this book and I appreicated the examples they used to explain complex concepts and data sets. Some topics covered are linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.
Courses
I can’t speak on all programs, but to be accepted into Galvanize you had to pass the admissions exam. Here are the courses I took to prepare:
- Data Science Prep Course (Galvanize)
- If you’re serious about going, I’d get the “Premium Prep”. Once you get pass the entrance exam, you’ll have to complete additional pre-course work — some of which is already included in the premium but not the free.
- Complete Python Bootcamp: Go from zero to hero in Python 3 (Udemy)
- Introduction to Python Programming (Udacity)
- Python for Data Science and Machine Learning Bootcamp (Udemy)
- Statistics & Probability (Khan Academy)
- JB statistics
- Not a course – but an INCREDIBLE stats refresher. He’s got a PhD in Stats and has taught intro courses in the past. It can be quick, but I would pause the video to work the problem out at my pace when necessary.
- codewars
- Coding challenge website that has wide variety of problems to solve across several languages, including Python. Difficulty starts at 8 “kyu” and it works backwards, so the lower the number, the harder they get. I would recommend starting this as in tandem with any of the Python bootcamp courses! I wish I did
Coming in Part III
You’ve got some solid actions items to help you start your journey! If you’ve come across other resources that would be helpful for others, be sure to drop them in the comments and I’ll add them to this post!
In the next and final post, I’ll speak about my first-hand experience going through the Data Science Immersive at Galvanize and life after completing the program!
meet sarah
Welcome! I’m Sarah. I started this blog to be a resource for others around a few of my favorite things: living in Colombia, DIY projects, places traveled, and day-to-day life. My hope is that it can a place of inspiration and encouragement to help you plan the next project or adventure of your own!
