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
Year One | ||
---|---|---|
Fall Semester | Credits | |
DATA6000 | APPLIED STATISTICS FOR RESEARCH | 3 |
DATA6150 | DATA SCIENCE FOUNDATIONS | 3 |
COMP6999 | TECHNICAL PROJECTS DEVELOPMENT | 3 |
Credits | 9 | |
Spring Semester | ||
Elective | 3 | |
Elective | 3 | |
Elective | 3 | |
Elective | 3 | |
Credits | 12 | |
Summer Semester | ||
Elective | 3 | |
Elective | 3 | |
Elective or Capstone or Thesis | 3 | |
DATA6999 or COMP7600 | CAPSTONE or THESIS | 3 |
Credits | 12 | |
Total Credits | 33 |
2-Year Option
Year One | ||
---|---|---|
Fall Semester | Credits | |
DATA6150 | DATA SCIENCE FOUNDATIONS | 3 |
COMP6999 | TECHNICAL PROJECTS DEVELOPMENT | 3 |
Credits | 6 | |
Spring Semester | ||
Elective | 3 | |
Elective | 3 | |
Credits | 6 | |
Summer Semester | ||
Elective | 3 | |
Elective | 3 | |
Credits | 6 | |
Year Two | ||
Fall Semester | ||
DATA6000 | APPLIED STATISTICS FOR RESEARCH | 3 |
Elective | 3 | |
Credits | 6 | |
Spring Semester | ||
Elective | 3 | |
COMP7600 or DATA6999 | THESIS or CAPSTONE | 3 |
Credits | 6 | |
Total Credits | 30 |
4+1 Option
Senior Year | ||
---|---|---|
Spring Semester | Credits | |
COMP5050 | MODERN COMPUTING | 4 |
Credits | 4 | |
Summer Semester | ||
MATH5100 | STATISTICAL THINKING | 4 |
Credits | 4 | |
Year One | ||
Fall Semester | ||
DATA6150 | DATA SCIENCE FOUNDATIONS | 3 |
DATA6000 | APPLIED STATISTICS FOR RESEARCH | 3 |
COMP6999 | TECHNICAL PROJECTS DEVELOPMENT | 3 |
Elective | 3 | |
Credits | 12 | |
Spring Semester | ||
Elective | 3 | |
Elective | 3 | |
Elective | 3 | |
DATA6999 or COMP7600 | CAPSTONE or THESIS | 3 |
Credits | 12 | |
Total Credits | 32 |
Electives
Course | Title | Credits |
---|---|---|
DATA6100 | DATA VISUALIZATION | 3 |
DATA6200 | DATA MANAGEMENT | 3 |
DATA6250 | MACHINE LEARNING FOR DATA SCIENCE | 3 |
DATA6300 | ADVANCED TOPICS IN LARGE LANGUAGE MODELS | 3 |
DATA6710 | APPLIED DEEP LEARNING | 3 |
COMP5100 | NATURAL LANGUAGE PROCESSING | 3 |
COMP5705 | DATA MINING | 3 |
COMP5710 | PRINCIPLES OF MACHINE LEARNING | 3 |
COMP7025 | Sports Analytics | 4 |
COMP7350 | BIG DATA SYSTEMS | 3 |