You’re clearly passionate about data. How did you get into it?
Ike: My educational background is in Physics and Electrical Engineering so I got to do some work with data (simulations and signal processing) while working on my bachelors degrees. Right after that, I spent two years working for a large technology consulting company with clients across various domains where I really started to understand some of the challenges from the business side and how data can be a game changer in how companies conduct business, develop new products, capture market share and understand their customers better.
With that in mind, I returned to graduate school where I was able to design my course work to enable me take classes from the Computer Science, Statistics and Operations Research departments and also correspondence graduate classes in Computational Finance. Some of those classes were very relevant in helping me understand some of the theoretical foundations in Machine Learning and optimization algorithms. In addition to that, MOOCs were just becoming popular at the time so I got to sample some of the early ML courses including Andrew Ng‘s popular machine learning course. I also got an opportunity to spend the summer between my graduate school years at a startup focused on employment science as their first Data Scientist intern. This gave me a chance to actually start implementing and building a lot of what I had learned on real data and real systems and the rest is history.
What was your data science bootcamp experience like, and what inspired you to write blogs about data bootcamps?
Initially, a lot of the writing I did on my personal blog [ Yet Another Data Blog ] was for my own consumption but my later writings became a means to share some of my experiences including those experiences with data science bootcamps. I also started Data Science Bootcamps as a vehicle to drive more visibility to the data science bootcamp ecosystem. There actually isn’t that much information out there, so I’m hoping this helps change that.
How is a data analytics or data science bootcamp different from a coding bootcamp?
What kinds of people succeed in a bootcamp? How do you measure success?
Success always has to be measured against a goal which is why it’s very important for prospective students seeking to enroll in a bootcamp understand what their goals are and how going through a bootcamp will help them either get closer to or achieve those goals. Two characteristics of most individuals that will do well in the bootcamp setting are grit and persistence. 6, 8 or 12 weeks isn’t as long as you might think especially when you’re essentially drinking from a firehose since you have so much new information coming at you.
What does the future of data bootcamps look like, and what is the biggest problem that bootcamps face?
Since the emergence of data bootcamps about three years ago, there’s been some evolution in how and what they teach, what sort of potential students they target and overall messaging. A lot of this had to happen for the data bootcamps to continue to stay relevant. Since then, there are now at least 20 full-time data bootcamps in the US/EU, a handful of other bootcamps with more flexible / hybrid offerings, lots of free MOOCs and online resources and about 500 Data Science / Analytics degree programs at Universities across Bachelors, Masters and Doctoral levels.
I think the biggest problem Data bootcamps face going forward is how to ensure they continue to deliver value in very quantifiable and measurable ways (placements or positive outcomes) .
What should more people know about bootcamps?
Bootcamps might look very similar on the surface but they’re actually all very different when you start digging into some of the details. It’s very important prospective bootcamp students do their research and understand these dynamics to ensure that they align with their goals.