On-site or online, the Master of Science (MS) in Applied Data Science program provides you with the skills to be an effective data professional in a rapidly growing field.

Upon completion, you will be able to identify, acquire, manage, present, analyze and interpret large amounts of data in a variety of organizational settings. You'll learn how to turn big data into smart data, improve operations efficiency, and understand the factors that turn consumers into customers.

The data science program offers close faculty and student interaction, with core courses ensuring that you acquire key critical skills from industry experts. In addition, the program offers a range of elective courses in various areas of data science from which you can build additional levels of proficiency and expertise while rounding out your education.

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Clarkson's MS in Applied Data Science

Data Science Curriculum

The 36-credit MS in Applied Data Science degree program consists of six 3-credit core graduate courses, four 3-credit graduate elective courses, and one 6-credit capstone course based on a sponsored project or internship. Full-time, residential students generally complete the program in three semesters. Students can complete the program at the Potsdam campus or 100% online. Additionally, online students can build part-time or full-time schedules that work for them. Part-time students generally finish the program in about two years.

Data Science Curriculum


Free introductory courses are offered online for accepted candidates from undergraduate backgrounds not covering calculus, programming and statistics


Required Courses (six):

  • IA510 - Database Modeling, Design and Implementation
  • IA530 - Probability and Statistics for Analytics
  • IA605 - Data Warehousing
  • IA640 - Information Visualization
  • IA650 - Data Mining
  • IA651 - Machine Learning


Elective Courses (four):

Elective courses are offered in a variety of areas, including but not limited to the following:

  • Modeling for Insight
  • Big Data Processing and Cloud Services
  • Introduction to Big Data Architecture and Applications
  • Strategic Project Management
  • Marketing Research Methods
  • Econometrics
  • Design of Experiments
  • Pattern Recognition
  • Stochastic Processes for Engineers
  • Artificial Intelligence
  • Human Computer Interaction
  • Digital Signal Processing
  • Computational/Machine Learning



The Capstone Project is a course centered on sponsored data science projects with interdisciplinary teams. Capstone projects, depending on project parameters could consist of a 2-unit seminar with a 4-unit project and/or be a mentored capstone of 6 total units. Depending on the nature of the capstone and its sponsorship, projects could be on-site fieldwork intensive. 

1 in 5 CU Alumni is a  CEO, president, owner or a senior  executive of a  company
Learning Experience

The Data Science program is offered both online and on-site for maximum flexibility. Take courses as a residential student in a traditional, on-site classroom setting at our main campus in Potsdam, NY.  Or, keep working and earn your degree online with classes available anywhere! Online class delivery is a mix of both synchronous (students attend class online together at the same time) and asynchronous (attend that week's class on your own schedule).

Online or on-site, your courses are taught by the same professors. They give you personal attention to help you learn as an individual and to prepare you for success in today’s intensely competitive market.

Clarkson is a very strong engineering school with amazing research opportunities. Faculty members are all very supportive and have a personal connection with all the students. The Data Science program is very rigorous here at Clarkson and has helped me prepare for my future plans.

Izzi Grasso, Data Science Student

Izzi Grasso

Careers in Data Science


Demand for data science and analytics professionals in the U.S. is quickly outgrowing the supply of such talent. The number of data science and analytics job listings is expected to increase by 364,000 openings to 2,720,000 by 2020, according to a 2017 report published by IBM. 

Data Scientists

While data scientists work within large data sets, the end result is more focused on technical skills, creating tools, systems and frameworks to help address theoretical situations and problems on a larger scale. Data scientists have an extensive background in statistics, computational methods and programming, enabling them to determine what approach is most appropriate for a given data set and analytical goal.


Class of 2022 Graduate Placement Rate of 100 Percent

A complete application consists of the following:

  • Online Application Form.
  • Resume.
  • Statement of Purpose.
  • 3 Letters of Recommendation.
  • Official Transcripts.
  • GRE Test Scores.
    • Applications will be read without GRE, but applicants should send if they have scores they believe enhance their application
  • For International Applicants, an English Proficiency Test is required.
    • Minimum Test Score Requirements: TOEFL (80) and TOEFL Essentials (8.5), IELTS (6.5), PTE (56) and Duolingo English Test (115).

A Note From Our Faculty

I am often asked by students and parents in what areas of human activity or industries is the data science expertise applicable or in demand. My standard answer is "Only in those that use data." In today's world it would be hard to think of any profession or industry that does not gather, generate, manage and analyze data, with the goal of extracting knowledge and value from it.

Dr. Boris Jukic, Director of Data Science Program

Boris Jukic

Meet Our Data Science Faculty

Dr. Boris Jukic, director of data science program

Boris Jukic, Data Science Director

Tyler Conlon, data science instructor

Tyler Conlon, data science professor


Joe Skufca, professor and chair of mathematics department

Professor Joe Skufca teaches a math class to his students on campus.


Sumona Mondal, associate professor of mathematics

Sumona Mondal speaking at the 2018 Healthy Water For New York & Beyond conference at the Beacon Institute.


Daqing Hou, professor and director of software engineering

Daqing Hou, associate professor of electrical and computer engineering, is photographed with a sensor.


Contact Us

Boris Jukic
Director of Business Analytics

Graduate Admissions