Number two, you need that practice. You need to actually get your fingers dirty, so to speak. You need to put them on the keyboard and actually code up these projects and get really good at implementing the things that you’re learning about when you’re doing just the theory.
And number three, you need the experience. You need to actually work with experienced data scientists, and you need to see them in action with implementing the craft.
You also need to practice doing that yourself. You need to get the experience as you develop those skills in order to become the kind of data scientist that every hiring manager is looking for. Every couple of months you will read about in the New York Times, in the Wall Street Journal, an article about “Why can’t anybody find these experienced data scientists, the kind of data scientists that people want?” Our philosophy here at Galvanize U is that nobody has gotten the recipe right. Nobody has figured out a master’s program that has the theory, but also has enough ample practice and enough of the experiential element to really get students coming out of the program to the point where they can be a top performing data scientist. The way we’ve designed this program is really focused on addressing these three core needs.
So an economics degree, a finance degree, these sorts of degrees are actually very, very helpful and a lot of data scientists actually come out of programs like this. So we’re actually going to be casting a wide net when evaluating what kind of training you’ve already had. As far as what skills you might want to either already have, or you might want to develop and learn a little bit about in anticipation for going through the application process, linear algebra and statistics is fantastic. It’s really the lifeblood of the mathematical understanding of a data scientist.
So refreshing your skills on that or maybe checking out some of the online resources to help, you know, get orientated in these subjects could be very helpful. And the program will take you from having taken it in the past, all the way to being an expert and there’s an entire component of the curriculum focused on developing these skills at the advanced level that you need them to be.
On the other side, even a data scientist who is focusing more on the data engineering and scalable implementation sites needs to understand the machine learning and the mathematics behind it because in order to implement that successfully, you need to understand what’s going on. And so you can help to make sure that the results that were gotten in prototype stage are actually preserved and maybe even improved by the time they get to production.
And last, you need to be able to communicate. It’s a common, common issue that somebody who has deep understanding maybe just in the machine learning or just in the data engineering, can’t necessarily communicate that understanding or the effectiveness of the model well, and that can lead to infelicities and trying to actually get your high power, really great product, or really great prototype to the floor and actually see it implemented. So being able to get the core skills you need for data communication is also very, very important for the most successful data scientists.
There are a lot of great online resources. There’s a lot of great data science communities out there. There are a lot of great data science hubs, like the one here in Galvanize, San Francisco, where there’s tons of people who are willing to help you get through, get orientated, go over the hurdles that you might be daunting you right now. And then you have the exciting end of the tunnel to look forward to where you say, “Wow, I’ve really accomplished something. I’ve really mastered this field,” and it can be very satisfying.