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Master’s Programs in Data Science and Analytics

1 April 2017 1,641 views 3 Comments
More universities are starting master’s programs in data science and analytics due to the wide interest from students and employers. Amstat News reached out to the statistical community involved in such programs. Given their interdisciplinary nature, we identified those that involved faculty with expertise in different disciplines to jointly reply to our questions. In 2015, for example, the ASA issued a statement about the role of statistics in data science, saying statistics is one of three foundational disciplines of data science. While the ASA has not issued a statement about the role of statistics in analytics, we assume statistics to also be foundational there. For this reason, we highlight the programs that are cross-disciplinary and engage statisticians. We will publish responses over a few issues of Amstat News. ~ Steve Pierson, ASA Director of Science Policy


University of Tennessee

Robert Mee is the William and Sara Clark Professor of Business, Department of Business Analytics and Statistics, Haslam College of Business, University of Tennessee. He is an ASA fellow who earned his PhD in statistics from Iowa State University.


Master’s in Business Analytics

Year in which first students graduated: 2011
Number of students currently enrolled: 38 full time, 6 part time. In the fall semester, approximately 80 (first- and second-year combined)

How do you view the relationship between statistics and data science?
Statistics informs both data collection and analysis. Other disciplines are involved with acquiring, managing, and analyzing data, but statistics gives particular attention to potential biases in both collection of data and in estimates of models. Statistics has the tools for quantifying uncertainty, understanding sources of variation, and confirming or contradicting hypotheses. Data science is centered on data and algorithms, as opposed to statistics, which begins with a problem to be addressed.

Describe the basic elements of your data science curriculum and how it was developed.
The core MSBA curriculum combines statistics, data mining/machine learning, optimization, database, and other computing skills, as well as giving the students a foundational understanding of the problems businesses address with analytics. Our students can choose electives in statistics, customer analytics, supply chain analytics, machine learning, or computer science to prepare them for their intended career direction.

We include a business perspective in our curriculum by maintaining close relationships with members of our Business Analytics Forum. In addition, many MSBA faculty members have extensive consulting and executive MBA teaching experience.

What was your primary motivation(s) for developing a master’s data science program? What’s been the reaction from students so far?
In 2009, our visionary department head brought an IBM white paper on the future of analytics to the attention of faculty. The paper emphasized applied statistics, business intelligence, and process optimization. At that time, we had two parallel master’s programs, one in statistics and the other in management science. We decided to combine these programs, creating a business analytics MS that now includes applied statistics, data mining, optimization, and Big Data tools.

What types of jobs are you preparing your graduates for?
Data scientist, analytics consultant, data analyst for supply chain or marketing. The title business analyst is not quite suitable, since this is often the title intended for less-quantitative MBA graduates.

What advice do you have for students considering a data science degree?
Take engineering calculus and learn some programming language. Pursue business analytics if you want to solve quantitatively-oriented problems and enjoy working with vast amounts of data to produce actionable insights that affect a business’s bottom line.

The letter “T” is sometimes used to characterize the business analytics masters, with the top of the T reflecting the breadth of this interdisciplinary degree and the vertical part indicating depth in one technical area. A statistics or computer science MS typically would have greater depth, but would lack the breadth of an MSBA.

Describe the employer demand for your graduates/students.
Last December, we graduated our 6th class of MSBA students. Through 2015, we have had 100% placement within three months of graduation. Companies making three or more hires include Amazon, The Boeing Company, Eastman Chemical Company, Hanesbrands Inc., Home Depot, and Regal Entertainment Group. We have also had many graduates work for consulting companies, including Accenture, Deloitte, EY, McKinsey & Company, KPMG, PWC, and several smaller firms.

Do you have any advice for institutions considering the establishment of such a degree?
Know your competitors. Consider the target incoming students, as this determines the length of the program. Consider your strategic advantages, especially ties with industry.

George Mason University

Robert Osgood is the director of the data analytics engineering program. He has expertise in developing and applying analytics to law enforcement in digital forensics, enterprise case management, cyber crime, counterintelligence, information security/technology, team leadership, and critical infrastructure protection.


Daniel Carr is professor of statistics and director of the statistics concentration in the data analytics engineering program. His driving interest is to create statistical graphic designs and software to address constraints posed by human cognition and challenges posed by new kinds of data and large data sets.


Master’s in Data Analytics Engineering

Year in which first students expected to graduate: 2014
Number of students currently enrolled: 290
Partnering departments: Volgenau School of Engineering (Lead): Statistics, Systems Engineering and Operations Research, Information Sciences and Technology, Computer Science
Program format: The MS Data Analytics Engineering (DAEN) program is an in-person program, although some courses are offered online. Our student body is a mix of full-time and part-time, both domestic and international. We are actively involved with our corporate partners and career services unit to offer internships for students.

Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.
The MS DAEN program is interdisciplinary. It revolves around a 15-credit core component with a 15-credit concentration component for a total of 30 credits.

Concentrations include applied analytics, business analytics, data mining, digital forensics, health care analytics, predictive analytics, and statistics. The core component consists of four courses, each taught in different departments, and a capstone.

Our admission criteria vary by concentration, but a minimum of one semester each of calculus, programming, and statistics is required. For the statistics concentration, three semesters of calculus, linear algebra, and probability are required.

What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?
The Volgenau School of Engineering at Mason, through working with its corporate partners and its advisory board, identified the need for data analytics education. Our investigation showed there was a growing demand for individuals with data analytics skills. Northern Virginia is a particularly appropriate area for employment in data analytics.

Student reaction has been extremely positive. Our spring 2017 enrollment of 290 students shows a definite demand for data analytics knowledge.

How do you view the relationship between statistics and data science/analytics?
There are two main components to data analytics: computing technology and statistics. Statistical analysis is what drives meaning from the massive data sets deposited with computing technology (hardware and software). The statistical component also includes visualization and data reduction.

What types of jobs are you preparing your graduates for?
Students are obtaining positions in a varied array of industries: cyber security, finance, government, information and knowledge management, software development, and e-commerce. Wherever data are collected, the need for analysis and statistical techniques is present.

What advice do you have for students considering a data science/analytics degree?
All departments (disciplines) look at the world from a certain point of view. Data analytics is not just computer science, or statistics, or business processes. It’s all of the above. So students looking for a more interdisciplinary view must seriously consider data analytics versus one of the traditional degrees.

It needs to be pointed out that data analytics requires more than just novice knowledge of com-puter science and statistics. For example, students need a solid statistical foundation normally found in a typical undergraduate statistics class. Also, knowledge of probability is also quite helpful. A minimum of one semester of calculus is also critical. Students need programming expertise. Any language will work, but Python is a particularly valuable language. Knowledge of database design and interaction is also desirable. Development of communication skills is essential in dealing with interdisciplinary stakeholders.

With these core components in place, a student can leverage his/her data analytics learning experience at Mason to the fullest extent.

Describe the employer demand for your graduates/students.
Our interaction with our corporate partners and exit survey data show a significant demand for Mason graduates. The finance, government, and technical sectors have employed our data analytics graduates.

Do you have any advice for institutions considering the establishment of such a degree?
The program must be interdisciplinary. No one owns data analytics, but everyone uses data analytics, so everyone needs to be a stakeholder. At Mason, the department of statistics is housed in the engineering school, along with the department of computer science, department of information sciences and technology (IT management), and department of systems engineering and operations research (predictive analytics). While each department created its own concentration with its own set of prerequisites, a common set of core courses was developed with minimal prerequisites, and each department contributed a course to that core.

University of Minnesota

Cavan Reilly earned his PhD in statistics at Columbia University in 2000, after which he joined the faculty in biostatistics at the University of Minnesota, where he has remained. Over the last several years, he has transitioned to working on more applied problems with an emphasis on clinical research on infectious diseases.


Dan Boley is professor of computer science and director of the graduate studies for master’s of science in data science program at the University of Minnesota. His research interests include computational methods in linear algebra, scalable data mining algorithms, algebraic models in systems and evolutionary biology, and biochemical metabolic networks.


Master’s in Data Science

Year in which first students expected to graduate: 2017
Number of students currently enrolled: 34
Partnering departments: Computer Science and Engineering (Lead), Statistics, Public Health (Division of Biostatistics), Electrical and Computer Engineering
Program format: Combination/ (distance learning option available)/typically traditional full-time, but a part-time option is available. Assistantships are available, but not guaranteed. Thirty-one credit hours required; six credit hours over two semesters are for a cumulative research project.

Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.
Our data science curriculum is intended the fill the spaces between algorithmics, statistical analysis, and modern computing infrastructure (these are the three core areas of our program). Finding the appropriate balance between these components and not simply duplicating and re-labeling existing opportunities for students was our guiding principle.

The curriculum was developed jointly by faculty from computer science and engineering, the school of statistics, electrical engineering and computer science, and the division of biostatistics in the school of public health. Through a series of meetings open to anyone interested, a consensus emerged that our program would focus on providing students with rigorous training in statistical methodology combined with a practical focus on computational feasibility in the age of Big Data, informed by a contemporary understanding of the possibilities engendered by the latest developments in hardware.

To distinguish our degree from the more business-oriented degree more commonly offered by many institutions around the country (including ours), we opted for a rigorous degree demanding a solid background in computing (3–4 semesters) and math/stat (3–4 semesters) at the undergraduate level as a minimum requirement. The other essential ingredient in our degree is a research component consisting of a two-semester capstone project.

What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?
This degree was created to meet the demand from companies for practitioners with solid training in scalable computing methods combined with a solid understanding of statistical issues and methods. Individual programs already had solid curricula in place to address various components of data science, but it was hard for students to package a program with the necessary elements without a cross-disciplinary degree. Many students in all contributing programs were already trying to assemble plans of study that would provide them with the necessary expertise one would expect from a degree in data science; this program has formalized that training. The response from students thus far has been very positive.

How do you view the relationship between statistics and data science/analytics?
We see statistics as but one component of a well-balanced program in data science. Some classical statistical techniques that served science well in the 20th century are simply not up to the task of dealing with data sets from the 21st century. This has led to extensive cross-fertilization between topics traditionally viewed as more within the realm of computer science and electrical engineering than statistics and ideas of great interest to statisticians (e.g., machine learning and causal inference). While such developments are positively affecting the practice of statistics and computer science, there are still opportunities to advance science that require perspectives and skills beyond what is possible in the context of a statistics curriculum or a computer science curriculum. As such, we see the relationship as complementary.

What types of jobs are you preparing your graduates for?
This is a new program, but our students have found internships at various companies employing sophisticated technology in the area, including health insurance, retail, and major social media companies on the coasts.

What advice do you have for students considering a data science/analytics degree?
Such students should strive for a balance between the three core areas identified above during their undergraduate education. Less balance is appropriate for a student specializing in computer science or statistics. The usual calculus sequence and linear algebra are still essential. A year of probability and statistics and a year of data structures and algorithms are becoming prerequisites. An internship is always helpful.

If a student is interested in graduate-level training in a field involving machine learning, data analytics, artificial intelligence, or data mining, having a solid computing background will be essential to implement anything novel. However, a solid statistics background will be essential to ensure that whatever is implemented gives statistically reliable predictions. Many employers have realized that a computing background alone is not sufficient for their next level of data systems development.

Do you have any advice for institutions considering the establishment of such a degree?
Ensure there is no obvious path for students to accomplish the same Stat/CS curriculum through an existing degree (unless you are prepared to simply re-label the existing degree). Make a choice between a regular graduate program with a choice of courses from a short list of requirements on the one hand and a cohort program where all students take the same courses together in sync.

Bentley University

Mingfei Li is an associate professor at Bentley University, where she has been a faculty member since 2008. She is currently serving as the MSBA program director and coordinator of the business analytics certificate and concentration programs. Her research interests include health analytics and sequential predictions.


Master of Science in Business Analytics

Year in which first students graduated: 2016
Number of students currently enrolled: 142 for the MSBA degree, 95 for a business analytics certificate or concentration within other degrees
Partnering departments: Mathematical sciences
Program format: Bentley University is a private business university broken down into a school of business and a school of arts and sciences. The master of science in business analytics (MSBA) is the only graduate degree offered by a department (mathematical sciences) not in the school of business. The department of mathematical sciences provides the quantitative curriculum for the MSBA degree, as well as all the other analytic courses offered at Bentley in statistics, mathematics, quantitative finance, data mining, Big Data, and operations research. Consequently, the department of mathematical sciences is, by necessity, interdisciplinary.

Currently, our program is an in-person program, but has synchronous hybrid classes for some of the courses. The MSBA degree requires 30 credits, and many courses require student projects. We have both full-time and part-time students. Bentley also provides scholarship and assistantship for some outstanding students.

Please describe the basic elements of your data science/analytics curriculum and how the curriculum was developed.
Our program has introductory statistics as a program prerequisite. Students are not required to have coding/programming skills upon entry because we offer programming classes from our curriculum: SQL, data science for R programming, reporting and data visualization, Java programming, HTML, Hadoop, MapReduce introduction, etc.

The MSBA has six required classes: SQL, operations research, and four business-oriented applied statistics classes. Students can choose four elective classes from a list, which includes classes from disciplines representing areas of business applications such as computer science, marketing, finance, economics, management, and informational process management. We collaborate with other departments on these elective classes.

For topics like database development and management, we use courses offered by our computer science colleagues. For business context, we use courses offered by departments of the school of business such as finance, marketing, management, economics, and information management. So while the MSBA program is highly interdisciplinary across both schools in our university, the core focus on analytics led to the decision that the MSBA be managed in the mathematical sciences department.

Beyond the existing curriculum, we continue to develop additional analytical courses to enrich the program curriculum such as machine learning (using R and Python) and design of experiments for business.

What was your primary motivation(s) for developing a master’s data science/analytics program? What’s been the reaction from students so far?
Bentley began to offer analytic courses in the 1990s. In 2006, we began to offer a certificate program in business analytics in response to a strong demand for graduates with comprehensive skills centered on statistics and including computer sciences skills, business knowledge, and communications skills. With strong demand from both students and employers, we launched the MSBA degree program in 2013.

Our enrollment rapidly increased from our first class of 40 to the current 162 students. Applications have increased in a similar manner.

How do you view the relationship between statistics and data science/analytics?
Applied statistics is the core of both data science and analytics. Understanding data and knowing how to analyze data are essential in most of applications. Computer science knowledge and programming skills are necessary to facilitate most analyses. Understanding the business questions and stakeholders’ interests provide the guide to statistical thinking and planning for analysis. Therefore, in our program, we require that students develop competency and skills in all three areas: analytics (statistics and operations research), computer science (related to data management and computing), and business (context for application).

What types of jobs are you preparing your graduates for?
Our graduates are hired by companies across different sectors, both private and nonprofit. Because of the wide applicability of business analytics, graduates have a variety of job titles such as business system analyst, data scientist, analytics consultant, data analytics adviser, research analyst, senior modeling analyst, business analyst officer, and business analyst. Employers of our students include CVS, National Grid, Toys R Us, Accenture, Deloitte, Ernst and Young, EMC, and Boston Children’s Hospital.

What advice do you have for students considering a data science/analytics degree?
Compared to a computer science degree or a traditional statistics degree, Bentley’s MSBA is a degree that integrates applied statistics with computer science, operations research, and business knowledge. Through this degree, students get interdisciplinary analytic knowledge with a practical understanding of business. Graduates can work with both business and technical teams to be a problem solver and innovator, providing decision support and business insights.

Describe the employer demand for your graduates/students.
There is a strong demand from employers for our MSBA students. From a survey of our first MSBA graduating class (88% response rate), 94% of graduates got full-time jobs within 90 days of graduation. The average annual salary for these graduates was $78,000, and the median salary was $80,000.

Do you have any advice for institutions considering the establishment of such a degree?
Being an interdisciplinary program, the MSBA needs support from multiple academic and administrative departments. Communication is crucial for the coordination and management of the program, as well as for curriculum development. A designated program director has been critical to coordinate all aspects of the program—from admissions to advising to placement—and to work directly with faculty colleagues, administrators, and students.

Students enrolling in the MSBA program have varied backgrounds, interests, and future goals. It is challenging to accommodate this degree of student variation in the curriculum, academic advising, and career services. The program director interacts with students personally throughout their enrollment in the program to understand their individual background and interests and advise their course selections and career preparation to help students achieve their personal goals.

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