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Silver Blog, March 2017Standardization and Specialization in Analytics, Data Science, and BI


We see beginnings of both standardization and specialization, with graduate analytics curriculum that covers math, statistics, CS, IT systems, and communications. We also see specializations in data science and BI, and verticals like marketing and healthcare analytics.



By Matt Ashare, OnlineEducation.com

At OnlineEducation.com, we’ve spent the past year interviewing experts in the field of analytics, and researching online degree programs in analytics, particularly at the graduate level. Our research points to the beginnings of what may be standardization and specialization within the field. We are seeing evidence of a baseline graduate analytics curriculum that covers proficiencies in mathematics, statistics, computer science, IT systems, and organizational communications. We’ve also found programs that are tailored to specializations like data science (DS) and business intelligence (BI), as well as programs that offer specializations in areas like business and marketing analytics, government policy analytics, and healthcare analytics.

Standardization in Academia

Today’s analytics programs integrate proficiencies from computer science/engineering, applied mathematics/statistics, and business communications into a core curriculum that typically also includes data mining and database fundamentals. This has two potential benefits: it can offer students a clearer path to a career in analytics; and it can provide businesses with a stronger pool of qualified professionals.

 

James Kobielus, IBM’s Big Data Evangelist, cites data science degrees as an example of this convergence. “Data science skills are in high demand,” he observed in an interview with OnlineEducation.com. He sees the degree as a way for professionals to “prove that they’re committed to and qualified for this career,” and to submit to “a structured curriculum to certify [they’ve] spent the time, money and midnight oil necessary for mastering this demanding discipline.”

Aaron Gowins, an NIH data scientist, spoke to this development in a separate interview. “Advanced degrees are often a requirement of employment,” he stressed. “But online courses are highly affordable and, in my opinion, are offering the best option for quickly developing relevant and useful skills. There is great demand for data scientists, and there have been relatively few quality advanced degrees being offered. Therefore, the current trend is toward preferring experience and demonstrated ability, which online courses have done a remarkable job providing.”

While Gowins remains unconvinced of the merits of formalized advanced degree programs in analytics, we now see several accredited, non-profit universities offering online master’s in analytics programs designed for working professionals, to address the very demand he references. Many of these programs offer the flexibility of full- or part-time enrollment and asynchronous instruction, which provides on-demand access to lectures and other course materials 24-7.

Specialization in the Field

Growth and standardization are often accompanied by specialization, and analytics appears to fit that pattern. The division of labor in analytics seems to break down into three broad areas that do overlap: jobs that focus on IT systems, database architecture, and BI platforms; jobs that focus on data mining for business/marketing purposes and other specialized applications in government, healthcare, and other fields; and jobs that focus on using advanced programming, algorithms, and artificial intelligence to make sense of big data.

In an OnlineEducation.com interview, Steve Miller, president of the consulting firm Inquidia, characterized BI as the “most IT-centric” analytics specialization. In his view, the line separating data analytics from data science is a bit fuzzier. “I’ve always seen analytics as more data-focused and computational than statistics, but less so than data science. When challenged to define the point that separates analytics from data science, however, I can’t, and argue feebly there’s a continuum from analytics to data science on a data/computation axis with endpoints [at] ‘not so much’ and ‘lots.’”

Even on the BI side of analytics, there can be diversification and specialization. As Jill Dyché, VP of Best Practices at SAS, explained in an interview, “A well-run BI program will include programmers, software experts, business analysts, data scientists, statisticians, and consultants. It will likely involve executives – Chief Analytics Officer and Chief Data Officer are newly visible roles in the BI space. And, of course, there are business users, who typically represent every major business unit in the company.”

Impacts on Academia

Our research indicates that online master’s in analytics programs are on a parallel track with the aforementioned developments. General data analytics programs are more prevalent those in data science and BI. These data analytics programs, which we group with business analytics programs because the curricula are so similar, target core proficiencies in applied mathematics, statistics, computer programming, data mining, and descriptive and predictive modeling. Some offer students the option of specialization through courses in subjects like marketing analytics, government analytics, and clinical research analytics. Data Science programs build off of the core analytics curriculum, incorporating advanced computer science coursework in highly technical areas like machine learning and artificial intelligence. BI programs emphasize database design, data warehousing, and dashboarding.

The curricular overlap in these programs appears to reflect the situation in the field. Christopher Wetherill, a data and decision science analyst at Safe Auto Insurance, offered a ground-level view of this in our interview with him. “We’ve found that we’re taking in far more data than we have the capacity to digest,” he explained. “So a big part of what I do is develop mechanisms to comb through those data.”

For Wetherill this includes creating “dashboards, reports, and models to allow end users to interact with data in ways that they haven’t previously been able.” He stressed that, “It isn’t enough to just know SQL, or to just know statistics, or to just know software engineering. Rather, you need to be comfortable shepherding the data from start to finish: you need to be able to query it, analyze it, prepare it, and present it, developing out a reproducible and automated data extraction-analysis-reporting pipeline as you go while still keeping in mind the business as a whole and how your data will be consumed and utilized within that context.”

In an environment like the one Wetherill describes, standardization and specialization may be more an ideal than an imminent reality. We have also found this to be the case in academia. Schools are attempting to define clear curricula for data analytics, data science, and BI programs, and these curricula are becoming easier to identify. However, these programs reflect the state of the professions, where clear lines are still coming into focus.

Bio: Matt Ashare is the Managing Editor of OnlineEducation.com, an independent publishing group devoted to researching and reporting on developments in online education and online degree programs.

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