[ faculty ]
All courses, faculty listings, and curricular and degree requirements described herein are subject to change or deletion without notice. Updates may be found on the Academic Senate website: http://senate.ucsd.edu/catalog-copy/approved-updates/.
Data science refers to mathematical models, computational methods, and analysis tools for navigating and understanding data and applying these skills to a broad and emerging range of application domains. A whole range of industries—from drug discovery to healthcare management, from manufacturing to enterprise business processes as well as government organizations—are creating demand for the “data scientist” with a skill set that includes a combination of computer programming, statistical analysis, and machine learning. Such a professional can create mathematical models of data, identify trends and patterns using suitable algorithms, and present the results in effective manners. The target systems can be biological (e.g., clinical data from cancer patients), physical (e.g., transportation networks), social (e.g., social networks), or cyber-physical (e.g., smart grids). In all these cases, the core knowledge base of the data scientist is the same and lies at the intersection of computing and mathematics, coupled with the skills to abstract, build, and test predictive and descriptive models. Depending upon the application domain, these capabilities often require a good understanding of underlying areas of physical or biological sciences as well as human cognition.
The major consists of 116 units; some lower-division courses be may used to fulfill general-education requirements. The required courses include mathematics (especially linear algebra and probability), computer science (programming, data structures and abstractions, and data mining), and statistics (estimation, testing, and exploratory data analysis). A twelve-unit lower-division course sequence in physics, chemistry, or biology will strengthen a major’s background in natural and physical sciences. The program includes twenty units of elective courses that will enable students to embark upon an in-depth exploration of one or more areas in which data science can profitably be applied. Alternatively, students can choose to explore the mathematical, statistical, and computational foundations of data science in even greater depth.
All majors will be required to undertake a senior project that will give them an opportunity to creatively synthesize much of what they have learned in the data science courses for addressing problems in chosen domains.
Students are expected to complete the following fifty-six units by the end of their sophomore year. All courses must be taken for a letter grade and passed with a minimum grade of C–.
- Data Science: COGS 9, DSE 10, DSE 20, DSE 30, DSE 80 (twenty units)
- Mathematics: Math 18, 20A-B-C (sixteen units)
- Computer Science and Engineering: CSE 20 or Math 15A and CSE 21 (eight units)
- Natural Sciences: Students must choose one of the following sequences (twelve units)
- Biological Sciences: BILD 1, BILD 2, and BILD 3 or BILD 10, BILD12, and BILD 20 or any three courses chosen from BILD 7, BILD 10, BILD 12, BILD 18, BILD 20, BILD 22, BILD 26, BILD 26, and BILD 28
- Chemistry: Chem 6A-B-C or Chem 11, 12, and 13
- Physics: Phys 2A-B-C or Phys 7, 8, and 10
Students must complete sixty upper-division units. All courses must be taken for a letter grade unless offered Pass/Not Pass only. A minimum grade of C– is required.
- Core Courses (thirty-two units): Math 183 or CSE 103 or ECE 109 or Math 181A, Math 189, DSE 110, DSE 120, DSE 170L, CSE 150, CSE 151, CSE 158
- Senior Project (eight units): DSE 196A-B
- Electives (twenty units)
- Any upper-division data science course not used to fulfill other requirements
- Any of the following: COGS 118A-B, 120, and 121; CSE 100, 101, 152, and 166; ECE 153, 156, 174, and 175A-B; ENG 100L; Math 173A, 173B, 181A, 181B, 181C, and 181E.
- Students will be expected to fulfill all prerequisites for all courses, which may entail additional course work beyond the data science major requirements.
- Students may petition to satisfy up to eight elective units using upper-division courses not on the list below but in an application domain of their interest.
- A maximum of twelve units of courses offered Pass/Not Pass only may be taken.
- Tutoring and independent study do not count (e.g., courses numbered 195, 198, and 199).
- A maximum of four units of ENG 100L may count.
Minor in Data Science
This minor is intended for students whose primary area of interest lies outside data science, but who are interested in acquiring competence in methods of data analysis. It requires completion of fifty-two units. Courses must be taken for a letter grade with a minimum passing grade of C–.
Lower Division (thirty-six units)
- COGS 9. Introduction to Data Science (4)
- DSE 10. Introduction to Programming (4)
- DSE 20. Introduction to Data Structures (4)
- DSE 30. Representations of Data
- DSE 80. Networked Life (4)
- Math 18. Linear Algebra (4)
- Math 20A. Calculus for Science and Engineering (4)
- Math 20B. Calculus for Science and Engineering (4)
- Math 20C. Calculus and Analytical Geometry for Science and Engineering (4)
Upper Division (twenty units)
- Math 183 or CSE 103 or ECE 109 or Math 181A (4)
- Math 189. Exploratory Data Analysis and Inference (4)
- CSE 158. Data Mining (4)
- DSE 170L. Data Visualization Laboratory (4) or COGS 133. Data Science in Practice (4)
- Upper-division data science course (4)