Data Science Master of Science

Leading to a Master of Science Degree in Data Science

Our global society produces an unprecedented amount of data. The tools to analyze and learn from these data are also developing at an accelerated rate. Companies and researchers in a diverse range of fields, including biomedical sciences, financial services and marketing, are seeking experts who can make the most of this data revolution. Our MS in Data Science will prepare you for a career in data science. Enter a high-paying job and advance your career in one of the fastest growing fields in today’s competitive job market.

Program Educational Objectives

The program educational outcomes for the Master of Science in Data Science that align with the listed graduate student learning outcomes developed by the Office of Institutional Effectiveness are as follows: 

  • Develop computational programming abilities to represent and explore data
  • Apply statistical data analysis techniques and quantitative modelling to solve data science tasks
  • Apply data munging/management principles to extract, load, process, and transform real-world data
  • Be aware of ethical consequences of data-informed decision making
  • Communicate data findings effectively to an audience, in oral, visual, and/or in written formats

Student  Outcomes

Wentworth published the following graduate student learning outcomes developed by the Office of Institutional Effectiveness in The Wentworth Model. Our graduate students will be able to demonstrate their mastery of these skills through the coursework required in the programs. The mapping of the Learning Outcomes to coursework will be as follows:

  • Core Knowledge: advanced knowledge in a specialized area consistent with the focus of their graduate program, including critical thinking and problem-solving.
  • Scholarly Communication: advanced proficiency in written and oral communication, appropriate to purpose and audience.
  • Professionalism: advanced intellectual and organizational skills of professional practice, including ethical conduct.
  • Research Methods and Analysis: quantitative and qualitative skills in the use of data gathering methods and analytical techniques used in typical research that is consistent with the focus of their graduate program.

Total credits for degree: 30 credits 

1-Year Option

Plan of Study Grid
Year One
Fall SemesterCredits
DATA6000 APPLIED STATISTICS FOR RESEARCH 3
DATA6150 DATA SCIENCE FOUNDATIONS 3
COMP6999 TECHNICAL PROJECTS DEVELOPMENT 3
 Credits9
Spring Semester
Elective 3
Elective 3
Elective 3
Elective 3
 Credits12
Summer Semester
Elective 3
Elective 3
Elective or Capstone or Thesis 3
DATA6999
CAPSTONE
or THESIS
3
 Credits12
 Total Credits33

2-Year Option

Plan of Study Grid
Year One
Fall SemesterCredits
DATA6150 DATA SCIENCE FOUNDATIONS 3
COMP6999 TECHNICAL PROJECTS DEVELOPMENT 3
 Credits6
Spring Semester
Elective 3
Elective 3
 Credits6
Summer Semester
Elective 3
Elective 3
 Credits6
Year Two
Fall Semester
DATA6000 APPLIED STATISTICS FOR RESEARCH 3
Elective 3
 Credits6
Spring Semester
Elective 3
COMP7600
THESIS
or CAPSTONE
3
 Credits6
 Total Credits30

4+1 Option

Plan of Study Grid
Senior Year
Spring SemesterCredits
COMP5050 MODERN COMPUTING 4
 Credits4
Summer Semester
MATH5100 STATISTICAL THINKING 4
 Credits4
Year One
Fall Semester
DATA6150 DATA SCIENCE FOUNDATIONS 3
DATA6000 APPLIED STATISTICS FOR RESEARCH 3
COMP6999 TECHNICAL PROJECTS DEVELOPMENT 3
Elective 3
 Credits12
Spring Semester
Elective 3
Elective 3
Elective 3
DATA6999
CAPSTONE
or THESIS
3
 Credits12
 Total Credits32

Electives

Course Title Credits
DATA6100DATA VISUALIZATION3
DATA6200DATA MANAGEMENT3
DATA6250MACHINE LEARNING FOR DATA SCIENCE3
DATA6300ADVANCED TOPICS IN LARGE LANGUAGE MODELS3
DATA6710APPLIED DEEP LEARNING3
COMP5100NATURAL LANGUAGE PROCESSING3
COMP5705DATA MINING3
COMP5710PRINCIPLES OF MACHINE LEARNING3
COMP7025Sports Analytics4
COMP7350BIG DATA SYSTEMS3