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11/15/2013
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Data Science Doesn't Belong In Business Schools

A data science professor argues why data science should sit outside of traditional, siloed university towers -- especially business schools.

For the last few years, major universities around the country have debated the issue of "data science" as a unique discipline. Specifically, the debate has centered around two positions: Either 1) universities just continue to teach the foundational topics of mathematics, statistics, and computer programming as they always have, and then leave the graduates to learn the rest on the job; or 2) data science is emerging as a unique discipline that deserves its own unique curriculum, and students should be allowed to obtain degrees in data science (and DS-esque areas like predictive analytics).

A survey of major universities around the country seems to indicate that option B appears to be winning.  And that is a good thing. 

After all, universities are really the farm system for the major leagues of data science. There is a talent gap, and it's good to see that we (the academic community) have realized that we cannot contribute to this talent pool in any meaningful way by continuing to teach foundational subjects like mathematics and statistics the way they have always been taught. Universities appear to be pivoting to meet the demands of the market. 

[ Five big data roles are emerging in the enterprise. Read How To Build An Analytics A-Team. ]

But, in many instances, the data science discipline is evolving within the university business school.  And this is troubling.

Data science is, pedagogically, much more like English than marketing. Why? Learning how to write well and use proper grammar is a core set of foundational skills that all college graduates must develop. We don't teach English in the college of engineering and in the college of business and in the college of science. We send everyone in the university to the English department. Then, students take these nascent writing skills and apply them in their engineering, business, or science courses. Good engineers and scientists must write well -- whether they want to or not.   

I would argue that today, the ability to work with data is becoming another core foundational skill that graduates of four-year accredited universities must have -- whether they want to or not. The rationale is pretty straightforward and simple: Data is ubiquitous to all sectors of the economy. Whether you are engaged in engineering, marketing, chemistry, finance, psychology, or political science, you will have to understand the basics of translating raw data into information to support the decision-making process -- yours or someone else's. 

This is not to say that all students want to become computer programmers or statisticians. They don't.  But they need to know some of the basics -- just as not all students want to become professional writers, but they have to know how to write. 

I like Vincent Granville's recent post regarding "vertical" data scientists, which he refers to as "fake," versus "horizontal" data scientists. According to Granville, "Vertical data scientists are the by-product of our rigid university system which trains people to become either a computer scientist, a statistician, an operations research or an MBA guy." He describes "horizontal" data scientists as such: "They combine vision with technical knowledge." I would add to this that they have some area of subject matter expertise -- they understand how to apply basic ETL (extract, transport, load) functions, programming, visualization and modeling skills to some "content domain."  Again, this could be engineering, marketing, chemistry, finance, psychology, or political science.   

And therein lies the formula for universities.

The discipline of data science should sit outside of the traditional siloed towers of universities. Embedding data science in the business school or in any content area is problematic for two primary reasons:

First, it is resource inefficient, like teaching English or math within each school on campus. Data science is a combination of mathematics, statistics, computer programming, and some area of application. Let the business school become an area of application of analytics -- because that is what it is.    

Second, the interdisciplinary classroom allows for exchanges of ideas and solutions not seen in a narrowly siloed classroom. When students studying economics, chemistry, physics, political science, sociology, and finance sit side-by-side in an analytics course, the students will offer alternative perspectives to problem solving and issue resolution, which are, in many instances, far more instructive and valuable for the other students than anything the professor could have planned.  Students then take their learnings from their fellow students in the other disciplines back to their home departments. In our own university, where we have these interdisciplinary classrooms in applied analytics, we have seen, for example, finance majors using risk modeling approaches to solve nursing problems related to likelihood of patient re-admittance, and actuarial students using survival analysis concepts from epidemiology to solve problems related to insurance underwriting.

Michael Rappa, director of the Institute for Advanced Analytics at NC State, has figured this out. The institute is a template for interdisciplinary success, where subject matter experts from departments across campus (and from outside of academia) come to the institute to teach for a specified number of weeks in their areas of expertise, bringing the front line of data science into the classroom.  Says Rappa, "Isolating analytics and data science in the business school is the surest way to kill it."

There is no question that data science is emerging as a unique discipline within universities around the country. Where data science ultimately resides within the university will influence those graduates' ability to compete in the major leagues of data science.

Emerging software tools now make analytics feasible -- and cost-effective -- for most companies. Also in the Brave The Big Data Wave issue of InformationWeek: Have doubts about NoSQL consistency? Meet Kyle Kingsbury's Call Me Maybe project. (Free registration required.)

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dmatheny620
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dmatheny620,
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11/30/2013 | 12:06:13 PM
Relevancy is the goal
Relevancy is all important.  As long as math departments continue to teach Statistics and not applied Data Science, business and other non-math students will continue to scratch their heads about why they are learning statistics and consequently forget the material very quickly.  I teach Business Analytics within a business program because its what business students need.  The course includes the foundations of statistics and analytical software but only in the context of applied analytics using business cases and problems.  I feel the article minimizes the importance of relating this topic to a specific discipline and fails to state how to accomplish this.  Relevance can usually only be taught within the students' discipline.  A generic Data Science 101 course taught in the math or computer science departments can't do that...so business students need Business Analytics and Biology students need Biological Analytics, etc.  Its not redundant, it makes it relevant and applicable to students--and that is key to true understanding and for students to take it to industry.

 

 

 
jpriestley301
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jpriestley301,
User Rank: Apprentice
11/20/2013 | 12:41:21 PM
Re: But Do Teach Communication and Business Skills
Beth - you raise a good question.  In my opinion, Statistics is inherently interdisciplinary and all students should understand the basics of statistics - the concepts of confidence intervals, simple ttests, regression, etc - but ultimately, "Statistics" does not equal "Data Science".  The latter is more comprehensive - understanding where data comes from, how to organize it, and then how to transform it into meaningful information - typically using software like SAS.  While Statistics plays an important role there, Data Science integrates a level of Computer Science/Programming, and application that I dont think traditional Statistics courses address.  
Yukon
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Yukon,
User Rank: Apprentice
11/20/2013 | 9:48:35 AM
it could be worse
I am envious of data science residing in an Institute for Advanced Analytics as at NC State and obviously having the backing of the administration.  I gather the Dept. of Statistics at NC State is on board with this.  Frankly, I am even a bit envious of having data science located solely in a Business School silo.  An even more painful alternative is to have mini-silos in departments across campus, each with their local "expert" on data science who may be a "star" in their field doing cutting edge stuff (e.g., sociology, psychology, political science, industrial engineering, finance, transportation engineering, education leadership, etc.).  What passes muster in these areas as quality data science work often times is cringe-worthy.  We lost the battle of a centralized, powerful statistics unit many moons ago (stat methods being taught locally in units such as those mentioned above) and I would hate to see the same thing happen to data science/predictive analytics as colleagues in other units jump on the big data bandwagon.  We are obtaining and continuing to obtain external funding to support our efforts which is perhaps the strongest approach to getting administration backing.
RichardS154
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RichardS154,
User Rank: Apprentice
11/20/2013 | 5:27:09 AM
Possession is 9/10-ths of the law
Of course if b-schools get there first while math/stats/cs departments dither, . . .
Li Tan
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Li Tan,
User Rank: Ninja
11/19/2013 | 10:46:44 PM
Re: But Do Teach Communication and Business Skills
@Beth, in my opinion, in addition to Stats 101 course, the marketing students should take at least some other courseware about how to use the big data analytics tools. They don't need to know a lot of technical details but at least they should be aware of the power of big data and how to do basic analytics. The real-life case study would be helpful to build the sense of using big data in their future daily work.
BethSchultz
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BethSchultz,
User Rank: Apprentice
11/19/2013 | 11:32:38 AM
Re: But Do Teach Communication and Business Skills
@Li, do you think it's enought to require marketing students, say, to take Stats 101 and call it a day. Or should marketing students today be exposed to additional analytics-related coursework as well?
Li Tan
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Li Tan,
User Rank: Ninja
11/17/2013 | 10:44:27 PM
Re: But Do Teach Communication and Business Skills
I cannot agree more on both of you. In modern days, we cannot survive long and go further without having composite set of knowledge. Furthermore, data is essential to almost all scienctific disciplines. The marketing students do not need to grasp the details of MapReduce, but they need to have the sense of what data really means - it's not just about digits and structured tables but in most of the cases, it's about unstructured data - information from twitter, blog, etc.
jpriestley301
IW Pick
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jpriestley301,
User Rank: Apprentice
11/17/2013 | 6:45:12 PM
Re: But Do Teach Communication and Business Skills
Brian - I hear your concern about the degree taking "12 years".  My point was that students who are interested in sociology, psychology, finance, or theater - should all have to take a basic data science course.  This does not mean that a European History major needs to take multiple courses in Hadoop and Python...but rather they should be expected to be as "data fluent" as an engineering major is expected to be "literate" and learn how to write well.  Then, of course, for the students who want to actually pursue a career in Data Science - they take the deeper curriculum in Map Reduce, Programming, Text mining...and then have an area of application - like a minor course of study.  In the end, my position is that data science skills should no longer be an exotic area of campus for the "nerd herd" but rather main-streamed into all General Education Curricula.
Brian.Dean
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Brian.Dean,
User Rank: Ninja
11/17/2013 | 2:25:53 PM
Re: But Do Teach Communication and Business Skills
Yes I agree to all of the blog's points and your point as there should be a mix of finance etc, programming and communication. But my concern is that students are going to have to spend a very long time in college to do all of this, maybe 12 years or so. And maybe if everything is compressed into 4 years then the resulting degree is going to be like an IT degree relative to a CS degree.
Laurianne
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Laurianne,
User Rank: Author
11/17/2013 | 1:45:53 PM
Business School Isn't For Everyone...
Not everyone wants to go, or can, go to business school. I agree it would be a shame to silo that expertise in the business school. See a related column with a supporting POV: Want Big Data Success? Hire A Biologist.
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