The State of Analytics Degrees in Universities
By Thomas H. Davenport, Nov 15, 2018
If you want to hire students from universities with strong analytical skills, you need to know the landscape of available programs and skills. Some believe that universities move slowly, but many were swift to respond to the first wave of demand for professionals with analytics skills. Many business schools, for example, quickly designed master’s programs in analytics by drawing from strengths in established disciplines, including statistics and operations research. They also brought in faculty from marketing, finance, and organizational behavior to show how analytics can relate to those specific business disciplines. These offerings typically involve both business and analytics skills, so they are not as commonly found in undergraduate degrees.
Demand for analytical skills is high, of course, and Master’s degrees in business analytics currently are among the most popular new offerings in business schools. North Carolina State University debuted what might have been the first master’s in advanced analytics in 2007; today, according to estimates from AACSB International, there are more than 400 analytics degree programs offered by nearly 220 business schools worldwide. An article on the Poets & Quants website declared, “Without a doubt, the business analytics master’s is the belle of the specialty degree ball.”
For companies hiring graduates of analytics master’s degrees in business schools, it’s important to be aware of the differences among programs. Some are focused on applications of analytics, others on detailed analytical methods. Some have mostly classroom training, and others focus heavily on company projects and internships. Some focus heavily on statistics, while others include related (and important) subjects like data management, project management, and communicating effectively about analytics. Some are online, and some are face-to-face. A quick look at the curriculum of a program will enable you to classify its strengths and shortcomings.
Beyond the First Wave of Analytics Degrees
If the first wave of analytics education involved specialty degrees in business analytics, the second wave involves data science, and the third wave centers on artificial intelligence (AI). Business schools have found it somewhat more difficult to respond to the second and third wave than the first one. Data science, for example, is not only relatively new and difficult to define, it has a strong computer science orientation. For that reason, many data science programs are offered by schools of engineering, information, or computer science. Data science programs tend to focus more on complex programming methods for handling and analyzing big data; data analytics programs are typically more focused on analytical/statistical methods, with somewhat less focus on programming and computer science.
In fact, some universities have multiple analytics programs residing in various colleges within the university. The variety of offerings can be confusing to both students and their potential employers. For example, the University of Minnesota and the University of California San Diego offer master’s degrees in both data science and business analytics. At Minnesota, the MS in business analytics is offered through the Carlson School of Management, while the MS in data science is run by the schools of engineering, liberal arts, and public health.
The University of California at Berkeley has at least six different degrees that are oriented to analytics. An MBA focus area in data science and strategy is offered by the Haas School of Business; the rest can be found within other departments and colleges, such as engineering, public health, and computer science. To help students navigate the many options, the university does maintain a datascience@berkeley webpage that describes the “rich ecosystem of data-science related research, teaching, and communities across campus.”
While data science degree programs have begun to proliferate, programs built around AI are still rare. Both Northwestern’s engineering school and the University of Georgia’s college of arts and sciences offer master’s programs in AI, and both MIT and Carnegie Mellon University’s computer science schools have recently instituted undergraduate programs focused on AI. Several schools, mostly in Europe—including the University of Gothenburg, the Free University of Amsterdam, and the Ecole Polytechnique in Paris—also offer master’s degrees in artificial intelligence. These programs are either contained within the computer science or engineering schools, or they draw from multiple schools within the university.
To my knowledge, no business schools in the U.S. have degree programs in AI. This is not surprising, given the paucity of faculty with expertise in AI (I teach a course in business applications of AI, but I’d be hard pressed to teach, for example, deep learning algorithms). Technical faculty who are able to develop AI programs often have been hired—at very high compensation levels—by private sector firms such as Google and Facebook. Some such faculty maintain university affiliations, but often not with business schools.
What Skills Do These Programs Inculcate?
Because AI programs are so difficult to put together, most business schools will concentrate on programs for analysts and data science professionals instead. In my opinion, these professionals must acquire at least four different types of expertise to be competent in their jobs.
Quantitative and statistical skills are the foundation of any analytics role. Anyone holding such a job must be proficient at general statistical analysis—at least up to and including logistic regression analysis. Such professionals also should also be able to analyze categorical data, probability and statistical inference, optimization, and basic experimental design.
If graduates want to be hired into specific industries or business functions, they also should master other quantitative techniques. These might include such methods as lift analysis in marketing, stochastic volatility analysis in finance, biometrics in pharmaceuticals, and informatics in healthcare fields. Some types of analysts—those involved in business intelligence or reporting work—may be able to get by without substantial statistical knowledge, but this lack probably would eventually limit their career progress.
Analysts also must know how to use the software associated with their type of analytical work, whether it is used to build statistical models, generate visual analytics, define decision-making rules, conduct “what-if” analyses, or develop visual dashboards. These tools once were offered only as proprietary packages from vendors, but are increasingly open source today. You may want to check whether the software your company uses is also used by the universities you want to recruit from.
Data management skills are just as important to analytical professionals as statistical and mathematical expertise. The “dirty secret of analytics” is that analytical professionals spend the majority of their time manipulating data—finding, integrating, cleaning, matching, and so on. And surveys suggest that the skill most commonly sought by data scientist employers is not expertise with a statistical program, but rather with SQL—a query language for data management.
Like analytics professionals, data scientists also perform data management tasks, but their tasks tend to be more complex and often rely on languages such as Python. Their jobs often involve taking relatively less structured data like text and images and creating the rows and columns of numbers that are well suited to statistical analysis.
The topic of data management also encompasses data privacy, security, and ethical issues. In fact, some master’s programs in analytics, including one from the University of Notre Dame, require students to take a course in ethics. I expect those will become more common over time.
Business knowledge and design skills enable analysts to be more than simple backroom statisticians. Analysts need enough general business background to understand the problems and processes they are analyzing, so they should be familiar with marketing, finance, HR, and new product development. They also should know how analytics can be used to drive business value, and they must have insight into the opportunities and challenges their employers are facing. Many of these skills and content domains are taught in MBA programs, but most business analytics degrees don’t require a lot of traditional MBA courses.
Relationship and communication skills are vital to the success of all analytical projects. Analysts who can advise, negotiate, and manage expectations will be able to work effectively with their business counterparts to conceive, specify, pilot, and implement analytical applications.
These skills are particularly critical when analysts must communicate the results of their work. Within the business, they will need to share best practices with their bosses and colleagues, while emphasizing the value of analytical projects. Outside the business, they will need to develop working relationships with customers and suppliers. They also might have to explain the role of analytics in meeting regulatory requirements—for instance, an analyst might use data to help a utility company make a successful bid to increase its rates for service.
Analysts who can “tell a story with data” are highly prized. The ability to communicate effectively about analytics is the single most sought-after capability among graduates from analytics and business intelligence programs, according to one survey of employers, and some schools realize this. For instance, “Communicating with Data” is a required course within the new master of business analytics program at the Massachusetts Institute of Technology’s Sloan School of Management.
In addition to mastering these four sets of skills in the classroom, students typically participate in internships or practicums, during which they work on real analytical problems for real organizations. Students not only gain experience in what it’s like to be analysts or data scientists, they also get a chance to meet potential recruiters.
Challenges for Producers and Consumers of Analytics Skills
For schools, the challenge is combining all these necessary components into a single degree program. It’s particularly difficult to cover all the material because the typical master’s in analytics is only one year or three semesters long—hardly enough time to adequately train students who arrive at the program lacking basic analytic skills. It is probably more feasible for business schools to target students who already have undergraduate degrees in quantitative fields and provide them with “finishing school” skills in business and communication.
Another challenge schools face is assembling faculty with expertise in all the relevant areas. Although universities can be very siloed and fragmented in their offerings, some draw on faculty from multiple schools to create their analytics programs. For instance, the master’s in advanced analytics at North Carolina State, which was intended to be cross-disciplinary from the beginning, involves faculty from statistics, mathematics, bioinformatics, computer science, and English departments. Such cross-disciplinary programs can be excellent for student learning, but are not always easy for schools to maintain.
For employers, the challenges include figuring out which schools offer the required skills, which ones hire the most talented students, and which are most likely to graduate students who want to work in your company or location. If you want to find out which students are most likely to perform well in your company, make sure you interview them—if for nothing else, to test their communications skills. And an ongoing relationship with faculty members in the programs you like will provide not only opportunities for help with your analytics projects, but the inside line on which students might be best for you to hire.
About the author
Tom Davenport is the co-founder of IIA. He is the President’s Distinguished Professor of IT and Management at Babson College, and a research fellow at the MIT Center for Digital Business. Tom’s “Competing on Analytics” idea was named by Harvard Business Review as one of the twelve most important management ideas of the past decade and the related article was named one of the ten ‘must read’ articles in HBR’s 75 year history. His most recent book, co-authored with Julia Kirby, is Only Humans Need Apply: Winners and Losers in the Age of Smart Machines.
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