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 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.
- 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.
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 Science and Engineering Electives (2 credits):
Students must take two courses chosen from the following list.
*Note: special topics courses in areas other than those listed will not be accepted.
- BMD_ENG 311-0 Computational Genomics
- BMD_ENG 395-0 Topics in Biomedical Engineering (Biomedical Applications in Machine Learning)
- CHEM_ENG 379-0 Computational Biology: Analysis and Design of Living Systems
- CIV_ENV 304-0 Civil and Environmental Engineering Systems Analysis
- CIV_ENV 377-0 Choice Modeling in Engineering
- CIV_ENV/MECH_ENG 413-0 Experimental Solid Mechanics
- CIV_ENV 480-1 Travel Demand Analysis & Forecasting 1
- CIV_ENV 480-2 Advances in Travel Demand Analysis and Forecasting
- CIV_ENV 495-0 Selected Topics in Civil Engineering (Data Analytics for Transportation and Urban Infrastructure Applications)
- COMP_SCI 348-0 Introduction to Artificial Intelligence
- COMP_SCI 394-0 Agile Software Development
- COMP_SCI 396-0 Special Topics in Computer Science (Computing, Ethics, and Society)
- COMP_SCI 396-0 Special Topics in Computer Science (Deep Learning)
- COMP_SCI 396-0 Special Topics in Computer Science (Interactive Information Visualization)
- COMP_SCI 396-0 Special Topics in Computer Science (Social Networks Analysis)
- COMP_SCI 396-0 Special Topics in Computer Science (Visualization for Scientific Communication)
- COMP_SCI 397-0 Special Projects in Computer Science (Rapid Prototyping for Software Innovation)
- ELEC_ENG 328-0 Information Theory & Learning
- ELEC_ENG 373-0 Deep Reinforcement Learning
- ELEC_ENG 395-0 Special Topics in Electrical Engineering (Optimization Techniques for Machine Learning and Deep Learning)
- ELEC_ENG 424-0 Distributed Optimization
- ELEC_ENG 433-0 Statistical Pattern Recognition
- ELEC_ENG 335-0 Deep Learning Foundations from Scratch
- ES_APPM 345-0 Applied Linear Algebra
- ES_APPM 479-0 Data Driven Methods for Dynamical Systems
- ES_APPM 375-1 Quantitative Biology I: Experiments, Data, Models, and Analysis
- ES_APPM 375-2 Quantitative Biology II: Experiments, Data, Models, and Analysis
- ES_APPM 472-0 Introduction to the Analysis of RNA Sequencing Data
- IEMS 307-0 Quality Improvement by Experimental Design
- IEMS 308-0 Data Science and Analytics
- IEMS 313-0 Foundations of Optimization
- IEMS 340-0 Field Project Methods
- IEMS 341-0 Social Networks Analysis
- IEMS 351-0 Optimization Methods in Data Science
- MAT_SCI 391-0 Process Design
- MECH_ENG 301-0 Introduction to Robotics Laboratory
- MECH_ENG 329-0 Mechanistic Data Science
- MECH_ENG 341-0 Computational Methods for Engineering Design
- MECH_ENG/CIV_ENV 413-0 Experimental Solid Mechanics
- MECH_ENG 441-0 Engineering Optimization for Product Design and Manufacturing
- MECH_ENG 469-0 Machine Learning and Artificial Intelligence for Robotics
- MECH_ENG 495-0 Selected Topics in Mechanical Engg (Sensory Navigation and Machine Learning for Robotics)