Analytics and Insight: Problem Solving & The Essence of Data Science
By Kevin Croxall, Ph.D. / Data Science

What is Data Science? What does a Data Scientist actually do? What do you look for in a Data Scientist? Where did you get those shoes? In the myriad of questions which I am asked as a Data Scientist, these stand out both in terms of frequency of ask and the earnestness with which they are asked. While business objectives (and job seekers, and training programs) desire simple and straightforward answers, reality is complicated by the fact that the answers are all highly nuanced and somewhat esoteric.

Well, all except that last question. There, the simple answer is Fluevog.

While putting together a presentation where I was asked to answer these questions for a group of students, I came across an infographic created by the IBM® BigInsight™ Team that summed up a Data Scientist as an amalgamation of Analytics and Insight. This resonated with how I have often thought about Data Science in my experience. Now certainly there are other features one would look for in a Data Scientist but these two stand out, in what I see are orthogonal manners.

**What do you look for in a Data Scientist?

What's a data scientist

Analytics, statistic, math, plots – these are all tools that are used as a means to an end and like most tools, they come with manuals. The instructions may look like gibberish to some, but they exist. Dictionaries exist to define the terms and help a user grasp the meanings; they are available to all. There is a marvelous democratization of knowledge. In particular, in a business setting the derivation of new laws of mathematics or the creation of novel statistical tests are rarely necessary. While not everyone may enjoy math, or find it intuitive, the mechanics are able to be learned and employed by a significant number of people. In other words, given an investment of time, analytics are an open book.

On the other hand, insight and intuition are incredibly difficult to teach. At some level, they rely upon the innate curiosity and thought patterns of an individual. Hiring managers and proto-Data Scientists alike are oft stymied here. Why? The underlying question they want to answer is essentially: How can I know if this person is able to solve complex problems in a convincing manner and is then able to implement that solution such that others can be benefitted.

I think back to my undergraduate years when taking freshman physics. This was a class required by many majors as a prerequisite. While the material clicked in my mind, I was drafted to be part of a large study group where, despite my best efforts, I never successfully helped some people understand the problems we were working. These were not unintelligent people; they would end up being successful doctors, chemists, advertisers and a myriad of other careers. They simply did not think in the same manner. They were unable to lay hold of the elusive insight that unraveled the twisted knot of the problems.

What does a Data Scientist actually do?

All people use data to some extent. Not all people have the wherewithal to rapidly place raw data in a heretofore uncontemplated context and apply it to solve a problem that, to this point, did not have an answer. Insight with the ability to use data to answer questions is a rare talent. That is what a Data Scientist does and why they are so valuable. Note that this is a very general statement. It does not qualify the type of data. It does not limit the field of inquiry. It does not guide the sort of statistical tests that should be known. As a result, Data Scientists come in a variety of guises.

One Data Scientist may be more attuned to the nuances of statistical tests whereas another may know the intricacies of neural networks. Some Data Scientists may be better coders and others might be great communicators. The common factor that unites them in a single category of humanity is the tenacious curiosity that leads them to find the insights that solve the problem before them. Despite where an individual Data Scientist’s strength may be, they are marked by an ability to quickly learn and grow – but learning, growth, and insight alone do not themselves define a job field.

What does a data scientist do?

What is Data Science?

There are many ways to define Data Science as a domain; likely as many ways as there are people to whom this question is posed at the moment. I would posit that this comes from the fact that to define the field of Data Science we must combine the skills, insight, and problem-solving in a business context. The exact mixture of these depends on the individuals involved and the questions that must be answered, or more to the point the questions that must be asked. In general, data science is the ability to master new domains and techniques with a critical process enables them to solve problems and to understand how things work. Simply put, data science is problem-solving in a digital environment.


Author

Kevin Croxall, Ph.D.

Kevin Croxall is Director of Data Science for Expeed Software. He is a data and research scientist with more than a decade of comprehensive experience in data science project design and implementation. He has a broad range of experience in software development geared toward pipeline development, statistical analysis, and data visualization and presentation.

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