A Data Science Solution to the Question "What is Data Science?"

January 8, 2016

(This article was first published on Engaging Market Research, and kindly contributed to R-bloggers)

As this flowchart from Wikipedia illustrates, data science is about collecting, cleaning, analyzing and reporting data. But is it data science or just or a “sexed up term” for Statistics (see embedded quote by Nate Silver)? It’s difficult to separate the two at this level of generality, so perhaps we need to define our terms.
We begin by making a list of all the stuff that a data scientist might do or know. We are playing a game where the answer is “data scientist” and the questions are “Do they do this?” and “Do they know that?”. However, the “this” and the “that” are very specific. For example, “Data is Processed” can range from simple downloading to the complex representation of visual or speech input. What precisely does a data scientist do when they process data that a programmer or a statistician does not do?
To be clear, I am constructing a very long questionnaire that I intend to distribute to individuals calling themselves data scientists along with everyone else claiming that they too do data science, although by another name. A checklist will work in our game of Twenty Questions as long as the list is detailed and exhaustive. You are welcome to add suggestions as comments to this post, but we can start by expanding on each of the boxes in the above data science flowchart.
Since I am a marketing researcher, I am inclined to analyze the resulting data matrix as if it were a shopping cart filled with items purchased from a grocery store or an inventory of downloads from a video or music provider. The rows are respondents, and the columns are all the questions that might be asked to distinguish among all the various players. Let’s not include sexy as a column.
You may have guessed that I am headed toward some type of matrix factorization. Can we recognize patterns in the columns that reflect different configurations of study and behavior? Are there communities composed of rows clustered together with similar practices and experiences? R provides most of us who have some experience running factor and cluster analyses with a “doable” introduction to non-negative matrix factorization (NMF). You can think of it as simultaneous clustering of the rows and columns in a data matrix. My blog is filled with examples, none of which are easy, but none of which are incomprehensible or beyond your ability to adapt to your own datasets.
What are we likely to find? Will we discover something like anchor words from topic modeling? For instance, it is necessary to work with multiple datasets from different disciplines to be a data scientist? Would I stop calling myself a marketing scientist if I started working with political polling data? Some argue that one becomes a statistician when they begin consulting with others from divergent fields of study.
What about teaching to students with varied backgrounds in universities or industry? Do we call it data science if one writes and distributes software that others can apply with data across diverse domains? Does proving theorems make one a statistician? How many languages must one know before they are a programmer? What role does computation play when making such discriminations?
What will we learn from dissecting the “corpus” (the detailed body of what we do and know summarized by the boxes in the above data science process)? Extending this analogy, I am recommending that the “physician, heal thyself” by applying data science methodology to provide a response to the “What is Data Science?” question. 
Hopefully, we can avoid the hype and the caricature from the popular press (sexiest job of 21st century). Moreover, I suggest that we resist the tendency to think metaphorically in terms of contrasting ideals. The simple act of comparing statisticians and data scientists shapes our perceptions and leads us to see the two as more dissimilar than suggested by their training and behavior. The distinction may be more nuance than substance, reflecting what excites and motivates rather than what is known or done. The basis for separation may reside in how much personal satisfaction is derived from the subject matter or the programming rather than the computational algorithm or the generative model.

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