Wednesday, September 30, 2015

Why Science’s Loss is a Gain for Data Science

Until recently there haven’t been any formal training pathways to become a Data Scientist. Most Data Scientists come from backgrounds in statistics or computer science. However, while these other career paths develop some of the skills listed above, they typically don’t cover all of them. Statisticians are very strong on the maths and stats side, but generally have weaker programming skills. Computer scientists are very strong in the programming arena, but typically don’t have as strong a comprehension of statistics. Both have good (yet different) data analysis skill sets but can struggle with creative problem solving, which is arguably the hardest skill to teach.

In the last couple of years a number of post-graduate courses (and even a couple of undergraduate degrees) have popped up around the world. However, it will take a few years before the graduates from these courses trickle out into the work force. How will we meet the projected demand in the meantime? At least part of the solution may come from an unexpected source: astronomy and astrophysics.

Modern day astronomers generally have a really good mix of most (if not all) of the skill sets sought after in a Data Scientist. They have a very good knowledge of maths and statistics, are highly computer literate and proficient with at least one programming language (Python being the current favourite). They have a very wide and diverse range of high-level data analysis skills, and are exceptional creative problem solvers (astronomy research is a bit like sitting in Sydney and trying to solve a murder mystery in London using only a pair of binoculars, so creativity and lateral thinking are a necessity). Many also have experience working with high performance computing and parallel processing.


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