To be fair, not all of these 23 Masters programs being offered by these highly regarded US Universities and College are purely Data Science Masters, but they are close and very helpful to becoming a data scientist – if that is your aspiration. The list was compiled by www.mastersindatascience.org and tapped other known sources that track these programs.
Listed 23 of these programs in alphabetical order below. Many of these choices can also be found in the helpful list compiled by Information Week and Data Informed’s Map of University Programs.
ASU’s nine-month program focuses on using analytics in day-to-day business processes and managing it effectively. Required courses include data mining, applied regression models, analytical decision making tools and business analytics strategy. The curriculum also includes internship opportunities and a capstone practicum project with local Arizona companies such as American Express and Intel. 30 credit hours.
Thanks in part to demand from companies in the Route 128 corridor, Bentley’s program is growing by leaps and bounds.
The core 10-class curriculum includes required classes on strategic marketing, statistics and marketing research; students also select three graduate-level elective courses in marketing and/or information technology.
Internships are encouraged but not required. All classes are held at night. 80 percent of students are currently part-time and around 50 percent are international students.
Carnegie Mellon’s MISM and MITM focus on three core areas:
The aim is to produce graduates cross-trained in business process analysis and skilled in predictive modeling, GIS mapping, analytical reporting, segmentation analysis and data visualization.
Students acquire hands-on knowledge through applied research experiences at Heinz College’s iLab. They must also complete a summer internship and a team-based practicum project with an external company.
Columbia’s smorgasbord of data science concentrations includes:
A M.S. thesis track is also offered.
During the program, students participate with research groups, labs and theInstitute for Data Sciences and Engineering (IDSE). IDSE aims to be the world-leading institution in research and education in the theory and practice of data science.
DePaul’s two-year curriculum focuses on providing students with advanced skills in data mining, multivariate statistics, machine learning, and database processing.
Once they have chosen their concentrations, students can pick from a wide range of electives, including courses at the Kellstadt Business School.
To foster real-world skills, students work on industry-sponsored data analysis projects and/or internships in the analytics field.
Drexel’s program is tailored towards students with an interest in quantitative methods, exploring and uncovering relationships through data analysis and using data to solve business problems. It is also suitable for MBA students seeking a second degree focusing on quantitative sciences.
Required courses cover topics such as business statistics, decision sciences, mathematical modeling, operations research and special topics such as data mining.
Students can also choose eight electives from a variety of fields (finance, economics, advanced statistics, etc.).
The full-time program can be completed in less than two years. 45 credit hours.
IU students majoring or minoring in business analytics are taught how to support business activities with data analysis.
Students must complete 15 credit hours, including two courses outside of the business analytics major.
IU also offers a research capstone project. With data as their raw materials, teams of students collaborate to solve a real business problem and present their findings to the faculty.
Sponsored by SAS, LSU’s program is modeled on the Institute for Advanced Analytics at North Carolina State.
The program works with real-world data, with a focus on data mining, forecasting, customer segmentation and predictive analytics.
Core courses cover advanced data management tools, applied statistics and operations research techniques.
During the year, teams of students collaborate with leading companies and government organizations on big data projects. They also learn the communication skills needed to present their solutions effectively.
MIT’s MBA allows candidates to create their own customizable curriculum. In addition to their studies and the mandatory first-semester “Core,” students participate in business-focused activities like:
Specialized tracks include Enterprise Management, Finance and Entrepreneurship and Innovation.
MIT has a plethora of research centers – including the MIT Center for Digital Business, the MIT Computer Science and Artificial Intelligence Laboratory and the Center for Computational Research in Economics and Management Science – and draws on its students for projects.
Students in MSU’s program focus on three core areas:
Core courses include business strategy, data mining, applied statistics, project management, marketing technologies, communications and ethics. In the final semester, students complete a capstone practicum in business analytics.
To help ease the path of professionals, MSU offers classes in evening, weekend and online formats.
NYU’s advanced business program is designed to teach students the role of evidence-based data in decision-making and understand how it can be used as a strategic asset.
Students attend five concentrated sessions in rotating global locations over a one-year period.
After completing four progressively advanced modules in data science, student teams are required to present a strategic capstone project.
In order to optimize classroom time, NYU employs distance learning between modules. However, all teaching is done in person.
NCSU’s interdisciplinary analytics program is an official STEM degree that is designed to be practical and real-world-oriented. It does not focus on theory or act as a preliminary step to a Ph.D.
During the course of their one-year study, NCSU students acquire real-world skills in applied mathematics, statistics, computer science and business disciplines. Initial classes focus on tools and foundations; subsequent topics include data mining, advanced modeling and geospatial analytics.
To encourage collaboration, students complete the curriculum in cohort groups of four or five members. Teams regularly receive personalized coaching to improve performance.
In the second and third semester, students work on an intensive practicum using data provided by industry and government sponsors. Many graduate with SAS product certifications.
Northwestern’s online MSPA program was launched in 2011. It covers areas like data mining, predictive modeling, and advanced statistics, but also works to help students hone business-focused skills like project management and communication, with the aim of preparing students to lead business initiatives based on data analysis. MSPA students will complete eleven courses overall: 7 required courses, 2 electives, a leadership course, and a thesis or capstone project.
Northwestern’s on-campus MSiA program was first offered in 2012, and combines math and statistical studies with advanced IT and data analysis. In addition to these courses, students are also required to complete:
Purdue’s MBA with a concentration in business consulting is designed to add depth to the broad base of management skills covered in the core MBA curriculum.
Recommended foundation courses include advanced business analytics, data mining and management of organizational data. Students can then choose from three electives:
Each student also participates in Purdue’s Launching Global Leaders initiative, a project devoted to leadership, communication and career development training.
Rutgers’ interdisciplinary program unites the fields of data management, statistics, machine learning and computation. It prepares students for careers as predictive modelers, data mining engineers and analysts in a wide variety of industries.
The curriculum includes eight analytics courses (four required; four electives) and six business courses.
Although most courses are located on the New Brunswick campus, some courses are available in Newark and Camden.
Stanford’s Information Management and Analytics track covers the principles underlying modern database and information management systems and methods for mining massive data sets.
Course topics include:
System-related topics include distributed systems, networking and security; applications-related topics include text mining, bioinformatics, web search and social media. Research projects and internships are recommended but not required.
Stanford’s key location in Silicon Valley has its benefits. For example, Stanford students are permitted to use Amazon’s EC2 cloud platform to do large-scale computing.
Formerly known as the M.S. in Quantitative Analysis, UC’s program combines math, business basics, analysis, and modeling, along with subject-area elective coursework on topics ranging from statistics and data mining to supply-chain management, operations management, finance, and epidemiology.
The curriculum covers core courses in optimization, simulation modeling, probability modeling and statistical methods.
Part-time students can choose to attend evening and late afternoon classes; all students are required to research, write and present a final capstone project.
The core of UConn’s program is built on ten years of an analytics partnership with General Electric. Graduates emerge with a solid real-world education in advanced business analytics and project management.
Students are required to complete:
Classes are held at night and on weekends; some courses are also available online. The program currently has a technology partnership with SAS and works closely with IBM.
Launched in the fall of 2013, UM’s program aims to provide students with a comprehensive understanding of the mathematical and statistical models and tools needed to analyze customer data for marketing purposes.
The curriculum includes:
Smith is the hub of ten Centers of Excellence and Research, including the Center for Complexity in Business. 30 credit hours.
Students in USF’s interdisciplinary program master the methods and technologies tied to strategic decisions; develop technical skills such as software development and statistical analysis, as well as the skills needed to effectively communicate their results.
Students are required to complete:
Like Stanford, USF benefits from its strategic location. Students develop software and run analyses on Amazon Web Services, and each receives his/her own server or servers to set up and manage.
UT’s program emphasizes the importance of business understanding. Students learn about technical techniques in the context of larger business problems and employ analytics in ways that optimize business processes.
The core curriculum focuses on analytics skills, including statistics, operations management, descriptive modeling, data mining, etc.
While learning those essentials, students also choose one out of two overlapping areas: either applied statistics or process optimization.
Students are required to complete an applied project with a client in government or industry. They are also strongly encouraged to participate in an internship during the summer session of the first year.
As of 2013, UT was considering expanding its concentrations to include supply train, customer analytics, healthcare and financial analytics.
The Master of Science in Analytics program at Villanova University prepares students to organize, analyze, and interpret data in order to make smarter business decisions. Students will graduate with knowledge of the latest analytics practices, how to uncover market trends through big data, and effective means of communicating technical findings to the business world.
Students are required to complete:
The entire Master of Science in Analytics program can be completed online in as little as 20 months. Courses are offered year round, and students balancing work and family can earn their degree part-time.