The requirements for the proposed M.S. in Data Science are structured so that undergraduate student would be able to pursue a five-year Bachelor’s/Master’s program, in which the Bachelor’s degree is awarded in any major offered at WPI and the Master’s degree is awarded in Data Science. Students enrolled in the Bachelor’s/Master’s program must satisfy all the program requirements of their respective B.S. degree and all the program requirements of the M.S. degree in Data Science. For students who will earn the Data Science B.S. degree at WPI, the “Integrative Data Science” core area requirement of the Data Science M.S. degree is waived. The students can choose to instead earn the corresponding 3 credits by taking any of the data science courses listed in the graduate catalog, including DS 501.
WPI allows the double counting of up to 12 credits for students pursuing a 5-year Bachelor’s/Master’s program. This overlap can be achieved through the following mechanisms. Students may double-count courses towards both their undergraduate and graduate degrees whose credit hours total no more than 40 percent of the 30 credit hours required for the M.S. degree in Data Science, and that meet all other requirements for each degree. These courses can include graduate courses as well as certain undergraduate 4000-level course as long as the undergraduate course is acceptable in place of a corresponding graduate course that satisfies a Data Science M.S. requirement.
In consultation with the academic advisor, the student prepares a Plan of Study outlining the selections chosen to satisfy the Bachelor’s/Master’s degree requirements, including the courses that will be double-counted. This Plan of Study must then be approved by the Data Science Program.
As a university-wide rule, the Bachelor’s/Master’s double counting credits can be applied for only while the student is an undergraduate student.
Admissions Requirements
Any WPI undergraduate student may apply to the Bachelor’s/Master’s program in Data Science. Students are encouraged to apply by their junior year so they can plan their 4000-level courses strategically and take advantage of the M.S. degrees.
Applicants are expected to have a strong quantitative and computational background including coursework in programming, data structures, algorithms, univariate and multivariate calculus, linear algebra and introductory statistics. Students in computer science, mathematics, business, engineering and quantitative sciences would typically qualify if they meet the above background requirements. A strong applicant who is missing necessary data science background may be admitted with the expectation that he or she will take the Data Science transition courses as needed, which include CS5007 if missing programming and algorithms background, and DS5002 if missing statistics background. Credits for these transition courses count towards the M.S. degree.
Double Counting Credits From 4000-Level Courses
For the following 4000-Level courses, two graduate credits will be earned towards the Bachelor’s/Master’s degree if the student achieves grade B or higher, or otherwise with the instructor’s approval. In addition, faculty may offer, at their discretion, an additional 1/6 undergraduate unit, or equivalently a 1 graduate credit, for completing additional work in the course. To obtain this additional credit, the student must register for 1/6 undergraduate unit of independent study at the 4000-level or a 1 graduate credit independent study at the 500-level, with permission from the instructor.
| Courses from Computer Science and Data Science |
|---|
| CS 4120 Analysis of Algorithms |
| CS 4341 Introduction to Artificial Intelligence |
| CS 4342 Machine Learning |
| CS 4432 Database Systems II |
| CS 4433/DS 4433 Big Data Management and Analytics |
| CS 4445 Data Mining and Knowledge Discovery in Databases |
| CS 4518 Mobile and Ubiquitous Computing |
| CS 4518 Mobile and Ubiquitous Computing |
| CS 4803 Biological and Biomedical Database Mining |
| CS 4084 Data Visualization |
| CS 4343/DS 4343 Deep Learning |
| CS 4344/DS 4344 Natural Language Processing: From Foundations to Large Language Models |
| CS 4345/DS 4345 Multi-Agent Systems |
| Courses from Mathematical Sciences and Data Science |
|---|
| MA 4235 Mathematical Optimization |
| MA 4603 Statistical Methods in Genetics and Bioinformatics |
| MA 4631 Probability and Mathematical Statistics I |
| MA 4632 Probability and Mathematical Statistics II |
| DS 4635/MA 4635 Data Analytics and Statistical Learning |
Other 4000-level courses not listed above, including 4000-level independent study courses, require a petition and approval from the Data Science Graduate Committee before they can double-count for the Bachelor’s/Master’s degree.
Restricted Undergraduate and Graduate Course Pairs
Some undergraduate and graduate courses have significant overlap in their content. The following table lists these courses. A student can receive credit towards their M.S. degree for at most one of the two courses in any row of this table.
| Courses from Computer Science and Data Science | |
|---|---|
| Undergraduate Course | Graduate Course |
| CS 4341 Introduction to Artificial Intelligence | CS 534 Artificial Intelligence |
| CS 4342 Machine Learning | CS 539 Machine Learning |
| CS 4432 Database Systems II | CS 542 Database Management Systems |
| CS 4433/DS 4433 Big Data Management and Analytics | CS 585/DS 503 Big Data Management |
| CS 4445 Data Mining and Knowledge Discovery | CS 548 Knowledge Discovery and Data Mining |
| CS 4518 Mobile and Ubiquitous Computing | CS 528 Mobile and Ubiquitous Computing |
| CS 4802 Biovisualization | CS 592 Biovisualization |
| CS 4803 Biological and Biomedical Database Mining | CS 583 Biological and Biomedical Database Mining |
| CS 4804 Data Visualization | CS 574 Data Visualization |
| CS 4343/DS 4343 Deep Learning | CS 4343/DS 4343 Deep Learning |
| CS 4344/DS 4344 Natural Language Processing: From Foundations to Large Language Models | CS 554/DS 554: Natural Language Processing |
| Courses from Mathematical Sciences and Data Science | |
|---|---|
| Undergraduate Course | Graduate Course |
| MA 4631 Probability and Mathematical Statistics I | MA 540 Probability and Mathematical Statistics I |
| MA 4632 Probability and Mathematical Statistics II | MA 541 Probability and Mathematical Statistics II |
| DS 4635/MA 4635 Data Analytics and Statistical Learning | MA 543/DS 502 Statistical Methods for Data Science |
Satisfying Data Science Core Areas
Bachelor’s/Master’s students can use the Bachelor’s/Master’s credits to satisfy a core area requirement if any of the following conditions is met: (1) The undergraduate course under consideration, either used to earn 2 or 3 graduate credits, must appear in one of the tables above, and the corresponding graduate course must satisfy the core area requirement. (2) The undergraduate course or independent study/project work is not in the tables listed above but it is deemed to satisfy the core area. This requires submitting a petition along with a detailed course description and syllabus to the Data Science Program for final decision.