If you’re thinking about a Data Science Bootcamp, there are three things you need to know. I don’t presume that my three things can be your three things or that that they can be anyone else’s. After all, we all live different lives and have different experiences. However, to me, Python, Statistics, and the course pace our something you need to be mindful of. Particularly, if you’re taking an immersive course, where you’ll be in class 8 hours a day for three months straight.
You’ll hear this from several former students, Python is the language of Data Science. Don’t get me wrong, R has its place, but it’s rare to come across R in boot camps, not on university property. Did you read that little caveat there, “university property’? Academia loves R compared to the production world who hail Python. But this isn’t a Python vs. R post. Just be aware of where you take your Bootcamp will determine what language is preferred. Feel free to visit this Python vs. R infographic link below:
In my opinion, a good understanding of Statistics is way more important than a good understanding of Python. I did, I said that. At least for me, I was able to chug along with Python, but the Statistics, however, that I needed to do a deep dive into it after hours. I bought several books on Stats ( I listed some below), and I happened to read some of them. You see, just the mere act of buying them and downloading them to your iPad was enough to begin the osmosis process (tongue & cheek, if that was evident). Everything revolves around Statistics. You learn Python to do Statistics. The supporting libraries used in Python, help you do statistics. You’ll definitely need to wrap yourself around Stats and say “thank you, sir, may I have another” — if you get the 80’s reference there. Did I just date myself?
It’s gonna go by fast. Before you know it, you’ll be in the 11th week of your 13-week course and say to yourself, “Thank God, it’s almost over.” You may also be freaking out because the last payment is soon due and you’ve spent a good chunk of it on eating out every day of the 13-week course. No worries, you got this. By now, you’ll have so many modeling practices under your belt, you’ll be good to go. You can have conversations with other Data Scientists and, not only understand but contribute to the discussion. You’ve come a long way. The last third of your class, in my opinion, eases up, maybe you begin to understand the course material better, but it does. Giving you the time to start updating your resume, social profiles, brand site, etc.…
Statistics, Python, and patience. Those are my top three recommendations for anyone jumping into a Data Science Bootcamp. Perferrfily, get a good understanding of the first two and come to terms with the last. The first two are your tools, the third is part of the process. You don’t want to fight the tools you’ll be using, that’s a hard fight to win. Learning how to use the tools and at the same time using/applying your tools will be overwhelming. If this is you, rely on my third recommendation — patience.
I hope this brief read gives you the confidence to jump in and the advice to prepare for your decision. Feel free to add or correct me below.
