Data Science Major

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Program of Study
Data Science
Degree Type
Bachelor of Science

Program Distribution Requirements for the Data Science Major 

The distribution requirements for the BS degree in Data Science consists of a series of interdisciplinary courses in Data Science, fundamental courses in Computer Science, Mathematical Sciences, and Business, and a set of more advanced courses selected primarily from the three supporting disciplines: Computer Science, Mathematical Sciences, and/or Business. 

Program Educational Objectives

In support of its goals and mission, the WPI Data Science undergraduate program’s educational objectives are to graduate students who will: 

  • Bring together a community of diverse disciplinary backgrounds and experiential perspectives to promote creative solutions to critical real-world problems and advance knowledge at the cutting edge
  • Achieve professional success due to their mastery of Data Science theory and practice
  • Conduct impactful research and project work in data science and artificial intelligence tackling the world’s most challenging problems
  • Engage in discovery through purpose-driven project-based learning
  • Collaborate with partners both internally and externally in interdisciplinary projects
  • Become leaders in business, academia, and society due to a broad preparation in data science, computational thinking, mathematics, science & engineering, communication, and social issues
  • Pursue lifelong learning and continuing professional development
  • Use their understanding of the impact of data science on society for the benefit of humankind

Theme:

“Gather Information, Form Insights, Impact the World”!

Program Outcomes

Students graduating with a Bachelor of Science degree in Data Science: 

  • Have mastered foundational studies in business, computer science, and mathematical sciences
  • Have mastered advanced principles and techniques in at least one of the three disciplines
  • Can apply computational and mathematical knowledge to the solution of big data problems
  • Can communicate effectively across disciplines both verbally and in writing
  • Can locate, read, and interpret primary literature in data science
  • Can function effectively as members of an interdisciplinary team
  • Have an understanding of accepted standards of ethical and professional behavior
  • Have the ability to be a life-long independent learner 

Data Science Core Courses (Minimum 3/3 Units)

Students must complete the series of three DS core courses (DS 1010, DS 2010, and DS 3010)

Business Foundation Courses (Minimum 2/3 Units)

Business foundation courses must include 1/3 unit in entrepreneurship and innovation (OBC 1010, ETR 1100, MIS 3010, ETR 3633), and 1/3 unit in business analysis (BUS 2080 OR OIE 2081). One course from each group.

Computer Science Foundation (Minimum 3/3 Units)

Computer science foundation courses must include 2/3 units of introductory computer science (CS 1004, 1101, 1102, CS 2102, CS 2103, CS 2119, or CS elective courses with no more than 1/3 unit at the 1000 level) and 1/3 unit of algorithms (CS 2223).

CS elective courses at level of 3000 and above may be substituted for introductory computer science credits.

Item #
Title
Units
CS 2223AlgorithmsUnitsDepartment

Building on a fundamental knowledge of data structures, data abstraction techniques, and mathematical tools, a number of examples of algorithm design and analysis — worst case and average case — will be developed. Topics include greedy algorithms, divide-and-conquer, dynamic programming, heuristics, and probabilistic algorithms. Problems will be drawn from areas such as sorting, graph theory, and string processing. The influence of the computational model on algorithm design will be discussed. Students will be expected to perform analysis on a variety of algorithms.

1/3

Mathematics Foundation (Minimum 5/3 Units)

Mathematics foundation courses must include 2/3 units calculus (MA 1020, MA 1021, MA 1022, MA 1120, or disciplinary elective courses in MA). Students cannot take both MA 1020 and MA 1021 for credit. Students cannot take both MA 1022 and MA 1120 for credits.
2/3 units applied statistics (MA 2611 and MA 2612), and 1/3 unit linear algebra (MA 2071 or MA 2072).

Mathematics disciplinary elective courses may be substituted for introductory calculus credits.

Data Privacy and Ethics (Minimum 1/3 Units)

Choose 1/3 unit from following: CS3043, GOV 2313, GOV 2314, GOV 2315, GOV 2320, PY 2713, PY/RE 2731 or RBE 3100.

Natural or Engineering Sciences (2/3 Units)

Natural or Engineering Sciences 2/3 units of work chosen in Natural or Engineering Science (courses with prefixes AE, AREN, BB, BME, CHE, CE, CH, ECE, ES, GE, ME, PH or RBE count). 

Disciplinary Elective Requirements

Chosen from disciplinary elective courses in CS, MA, or BUS 

At least one course must be selected from each of the following categories: 

  • Data access and management (CS 3431, MIS 3720, CS 4432, CS4433/DS4433) 
  • Data mining/machine learning (CS 4445, CS 4342) 
  • Business modeling and prediction (MIS 4084, OIE 4430)

DS 4099: Special Topics in Data Science is counted as a disciplinary elective course.

Disciplinary electives must include at least 4/3 units at the 4000 level or above.

Item #
Title
Units
CS 4120Analysis of AlgorithmsUnitsDepartment

This course develops the skill of analyzing the behavior of algorithms. Topics include the analysis — with respect to average and worst case behavior — and correctness of algorithms for internal sorting, pattern matching on strings, graph algorithms, and methods such as recursion elimination, dynamic programming, and program profiling. Students will be expected to write and analyze programs. Undergraduate credit may not be earned both for this course and for CS 5084. 

This course will be offered in academic years ending in odd numbers.

1/3
Please note: 

Students who are double counting their data privacy and ethics requirements as a social science are required to take an additional free elective to reach the required 135 credits.

Data Science MQP (3/3 Units)

Data Science project (3/3 units) must have a MQP faculty advisor that has a formal collaborative appointment in the Data Science program 

 

 

Program Chart and/or Course Flow Chart
Data Science Major Program Chart
Data Science Major Course Flow Chart