IDS Subject Minor
A subject minor in Information and Data Science (IDS) may be elected by graduate students who are pursuing PhD degrees in any option. The IDS graduate minor is designed for students who wish to broaden their expertise and get a solid background in the following areas: Mathematics of Data Science, Machine Learning, Statistical Data Analysis, and Information Theory.
To receive the IDS graduate minor, students must satisfy the following requirements:
- Linear Algebra: ACM/IDS 104 or CMS/ACM/IDS 107
- Probability: ACM/EE/IDS 116 or CMS 117
- Statistics: IDS/ACM/CS 157 or ACM 118
- Machine Learning: CMS/CS/CNS/EE/IDS 155 or CS/CNS/EE 156a
- One course from the following list: Hum/Pl 45, CMS/ACM/EE 122, Ma/ACM/IDS 140 ab, CS/IDS 150 ab, ACM/EE/IDS 170, ACM/IDS 216, ACM/EE/IDS 217, Ec/ACM/CS 112, IDS/Ec/PS 126, ACM/IDS 154, IDS/ACM/CS 158, CS/CNS/EE/IDS 159, CS/CNS/EE/IDS 165, CS/IDS 121, EE/Ma/CS 126 ab, EE/Ma/CS/IDS 136, CMS/CS/IDS 139, CS/IDS 172, CS/EE/IDS 143, CMS/CS/Ec/IDS 144, EE/CS/IDS 160, EE/CS 161, CS/IDS 162, CS/EE/IDS 166, EE/CS/IDS 167, CS/IDS 178.
Note that all these courses have prerequisites that the student may need to take, depending on the student’s background. The prerequisites can be satisfied with equivalent courses with approval from the instructor.
All courses used for fulfilling the IDS graduate minor requirements must be taken on a graded basis and students must obtain a grade of B or higher on all courses. Courses that are used to satisfy the minor requirements cannot be used to satisfy the requirements in the major options.