Undergraduate Minor in
Information and Data Sciences
Undergraduate Option Rep
The Information and Data Sciences (IDS) minor is intended to supplement one of Caltech’s undergraduate degrees and is designed for students who wish to broaden their knowledge beyond their normal major or who may wish to a graduate program involving information and data sciences, but do not want to pursue a major in IDS. Students completing the IDS minor will have the phrase “minor in information and data sciences” added to their transcripts.
Information and Data Sciences Minor Requirements
- Computer Science Fundamentals. CS1; CS2; and CS38.
- Mathematics Fundamentals. Ma 3, Ma/CS 6a or Ma 121a.
Information and Data Science Core Requirements.
- Probability: ACM/EE/IDS 116.
- Linear Algebra: ACM/IDS 104.
- Statistics: ACM/CS/IDS 157.
- Machine learning: CMS/CS/CNS/EE/IDS 155 or CS/CNS/EE 156a.
- Signal Processing: EE/IDS 111
- Applications of Data Science. At least 9 units from the following list: Ay 119, BE/Bi 103, Bi/CNS/NB 153, Bi/CNS/NB 162, Bi/BE/CS 183, BEM/Ec 150, CNS/Bi/EE/CS/NB 186, CS/EE/ME 134, EE/CNS/CS 148, Ec/SS 124, ESE 136, Fs/Ay 3, FS/Ph 4, Ge/Ay 117, Ge 165, HPS/Pl/CS 110, SS 228. Other courses that include applications of data science may be substituted with approval from the option coordinator.
- Advanced Electives. At least 9 units from the following list: IDS courses numbered 100 or above, CS/CNS/EE 156ab, ACM 106b, ACM 95/100ab. Courses used to fulfill this requirement may not also be used to fill the any requirement above.
Courses used to fulfill requirements in the “Applications of Data Science” and Advanced Electives” requirements cannot be used to fulfill (i) a requirement for another major or minor; or (ii) the institute humanities and social sciences requirements. Any replacement of these courses must be discussed with the option administrator.
Pass/fail grading cannot be elected for courses taken to satisfy option requirements. Courses taken as part of the data science minor are counted toward the total 486 units needed for Institute graduation requirements.
Typical Course Schedule
A typical course sequence is to take CS 1 during freshman year; Ma/CS 6a, Ma 3, CS2 and CS38 during sophomore year; ACM/EE/IDS 116, ACM/IDS 104, CMS/CS/CNS/EE/IDS 155, and ACM/CS/IDS 157 during junior year; and EE/IDS 111 and the elective courses during senior year.
Starting in the sophomore year IDS students will be assigned a faculty advisor whom they should meet with regularly, typically once per quarter. Students in the program are advised by faculty interested in the information and data sciences from across the institute. This includes all of the faculty, as well as the following faculty that pursue data science-related research and participate in IDS advising: Justin Bois, Fernando Brandao, Shuki Bruck, George Djorgovski, Laura Doval, Frederick Eberhardt, Federico Echenique, Michelle Effros, Babak Hassibi, Jonathan Katz, Victoria Kostina, Heather Knutson, Tom Miller, Pietro Perona, Antonio Rangel, Mark Simons, Omer Tamuz, Andrew Thompson, Matt Thomson, Victor Tsai, David Van Valen, Zhongwen Zhan. Students seeking an IDS advisor should contact the undergraduate option secretary at email@example.com.