Pursuing Data Science Part I: How to Pick a Bootcamp

Before we jump into the resources, it’ll be helpful for you to know more about my background. I started learning Python eight months before the program started and that was the extent of my previous coding experience. If you asked me any kind of probability question, I would’ve been stumped. The highest math course I took was statistics and that was over 10 years ago. So if you’re already in a bootcamp feeling defeated or having second thoughts on if you’re going to be good enough to get through it — all I’m gonna say is if I can do it, so can you!

I worked in advertising for 10+ years. I was drawn to creatively solving business problems… and if I’m being totally honest, the lack of math classes compared to a business degree. Looking back this is pretty hilarious considering how much I’ve learned to love problem solving using math.

I held positions in media planning/buying and account management, but it wasn’t until the end of 2018 that I realized I wanted to work more closely with data. As in — not just to reference it for the campaigns we were creating but getting to actually dig into the data and all of its messy glory. There’s definitely more to it, but that’s the quick jist.

I graduated from the Data Science Immersive program at Galvanize in November 2019 and it was the hardest thing I’ve ever done. As tough as it was chugging-from-a-firehose-on-full-blast for three months, I can definitely say I walked away with new, hard skills (like coding in Python and SQL querying) and a better understanding of statistical analysis and the various machine learning methods. You might be thinking, “uhhh, yeah I WOULD THINK you’d get something from it!” but that’s not always the case as I’ve learned and heard from other students who’ve completed different programs. 

In February 2020 I accepted a Data Analytics position at a tech company. I know for certain I wouldn’t have landed this position had it not been for Galvanize. While this was a great route for me, know that it could be different for you! I was looking for opportunities where I could strengthen the foundation I gained while attending Galvanize. You might be ready to jump into predictive modeling. If you’re not sure what the difference is this Northeastern article does a great job explaining the difference between data analysis vs. data science:

Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions.

Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

Either way, honor what you want and need. That was really hard for me to do in the program. I felt very “less than” being surrounded by engineers and people with PhD’s in Physics or Statistics. I was also one of the few who was new to coding at all and in Python. It’s easier said than done, but do you and remember that your journey is going to look different than someone else’s.