For our May event, we cast our speaker net out-of-state and convinced Jim Gianoglio from Bounteous to make the trip from Pittsburgh to share his experience and his thoughts on the myriad paths that exist for perambulation from “analyst” to “data scientist.” Or, as Jim subtitled his talk: “the transfiguration from reporting squirrel to unicorn:”
It was a packed house for the event, as Jim walked through his various explorations of options for advancing his analytics skills into the world of data science, which he boiled down to three options:
- Entering a formal degree program (online or offline)
- Relying on the various online courses and content that are available for free or a nominal fee
- Attending a bootcamp.
Jim initially dabbled in online courses, but, ultimately, went for a formal degree through Carnegie Mellon. The pros of that approach:
- The cost and face-to-face schedule meant that, even as the going got tough, bailing wasn’t really an option.
- The in-person interactions with professors and students made for productive collaboration and deeper learning (…including on the subject of — wait for it — deep learning, presumably </editorial license>).
- The networking benefits — in a traditional sense, this would mean that Jim was set up to hop to another role following the program, but, in this case, it meant that two of his fellow students got hired by Bounteous!
- The cachet of having a Master’s degree from a school like Carnegie Mellon — that’s good for the resume!
Of course, there were also downsides:
- It was an intensive and exhausting two years, as Jim continued to work full-time throughout the program, while also having a wife and three young children.
- It wasn’t cheap. Jim did the math as to how/when he would expect a return on his investment, and it made sense.
- There were still some “dud” professors, which can also happen in the online world, but, when you find yourself calculating a “cost per hour” during a lecture and getting a little steamed, that can be disheartening.
While Jim opted for the in-person, formal degree program, he also discussed — and provided a number of resources — for other options (some of which he availed himself of both before and after his formal coursework):
- Online degree programs from accredited universities
- Open courseware and content — use resources like the Open Source Data Science Masters to put together your own curriculum!
- Bootcamps — although Jim warned that there is an explosion of these being offered, so the quality varies wildly, and bootcamps can make unrealistic claims (“Become a data scientist in just 14 weeks with our bootcamp!”)
Ultimately, there are an overwhelming number of options, which can be intimidating, but it also means that analysts can do some research and introspection and then figure out what is the best option for them!
Ultimately, with a little bit of statistics, some Python, and a little bit of R, you, too, can catch yourself speaking like a data scientist!
Jim shared his slides (with notes) here if you missed the event or attended and would like to reference the material. A smattering of resources he referenced and recommended are:
- An Introduction to Statistical Learning with Applications in R — a free, downloadable book
- The Data Science Specialization on Coursera
- Kaggle’s Data Science Education resources
- fast.ai online courses