Curriculum & Requirements

Curriculum Overview

The minor in data science and engineering consists of 8 courses:

• 4 core courses in Data Science and Engineering
• 2 studio courses
• 2 electives from a list of data science-oriented engineering courses.

Important Notes

• At least 4 courses must be unique to the minor, and may not be used towards the 21-unit major program, or towards other minors or certificates.
  (For students in Catalog year 2021 or before, at least 5 courses must be unique to the minor, and may not be used towards the 16-unit major program, or towards other minors or certificates.)
• Students are strongly encouraged to discuss curriculum choices for the DS&E minor with their major advisors before proceeding. See the advising page for more major-specific curriculum guidance.
• Courses with a grade lower than C- cannot be applied to the minor.

Minor Requirements

Data Science and Engineering Core (4 credits):

Students must take one course from each of the following areas.

*Note: Courses used towards the basic engineering requirement or unrestricted electives are not considered part of the major program. Courses below marked with * are currently listed as McCormick basic engineering courses. Be aware that required basic engineering courses vary by major. (This note only applies to students in Catalog year 2021 or before. Please ignore this note if you are in Catalog year 2022 or after.)

1. Programming Foundations

• *COMP_SCI 150 Fundamentals of Computer Programming 1.5
• *COMP_SCI 211 Fundamentals of Computer Programming II
• *COMP_SCI 230 Programming for Engineers

2. Statistics Foundations

• *BMD_ENG 220 Introduction to Biomedical Statistics
• *CHEM_ENG 312 Probability and Statistics for Chemical Engineering
• *CIV_ENV 306 Uncertainty Analysis
• *IEMS 201 Introduction to Statistics
• *IEMS 303 Statistics

3. Intermediate Programming and Algorithmic Thinking

• COMP_SCI 214 Data Structures and Algorithms
• *COMP_SCI 217 Data Management and Information Processing

4. Applied Machine Learning

• ELEC_ENG 375 Machine Learning: Foundations, Applications, and Algorithms
• *IEMS 304 Statistical Learning for Data Analysis
• COMP_SCI 349 Machine Learning

Data Science Studio Courses (2 credits):

Both of the following courses must be taken.

• DATA_ENG 200 Foundations of Data Science
• DATA_ENG 300 Data Engineering Studio

View course descriptions

Data Science and Engineering Electives (2 credits):

Students must take two courses chosen from the DSE minor elective list.

*Note: special topics courses in areas other than those listed will not be accepted.