Time to Degree Considerations:

Students who entered UC San Diego as First-Years:

Students must be on track to complete DSC 30 and DSC 40A prior to their 3rd year at UC San Diego to be on track for an on-time graduation.

Students who entered UC San Diego as Transfers:

Currently, the DSC major requires a minimum of 8 quarters for transfer students to complete. To remain on this 8-quarter transfer plan, transfer students must be on track to complete DSC 30 and DSC 40A prior to their 2nd year at UC San Diego.

Major Curriculum

The major consists of 112 units with 52 units from lower-division courses and 60 units from upper-division courses.

Lower Division

The lower-division curriculum includes calculus and linear algebra courses for 16 units, data science courses for 28 units, and subject domain courses for 8 units. The program includes 20 units of elective courses that will enable students to embark upon an in-depth exploration of 1 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.

Students who entered as freshmen are expected to complete the following 52 units by the end of their 2nd year. All courses must be taken for a letter grade and passed with a minimum grade of C–.

  • Data Science (28 units): COGS 9, DSC 10, DSC 20, DSC 30, DSC 40A-B, DSC 80
  • Mathematics (16 units): (MATH 18 or MATH 31AH), (MATH 20A-B-C or MATH 31BH)
  • Subject Domain Courses (8 units): Students must choose 1 of the following 2-course sequences:
    • Business Analytics and Econometrics: ECON 1 and ECON 3
    • Machine Learning and Artificial Intelligence: COGS 14A and COGS 14B
    • Science: BILD 1 and BILD 3
    • Social Sciences I: (POLI 5(D) or ECON 5) and POLI 30
    • Social Sciences II: SOCI 60 and USP 4

Upper Division

Students must complete 60 upper-division units. All courses must be taken for a letter grade unless offered P/NP only. All courses must be taken for a letter grade and passed with a minimum grade of C–.

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.

  • Core Courses (32 units): (ECON 120A or MATH 183 or MATH 181A), MATH 189, DSC 100, DSC 102, DSC 106, (DSC 140A or CSE 150A), (DSC 140B or CSE 151A), (DSC 148 or CSE 158(R))
    • For requirements that offer multiple course options, students must choose 1 of the courses to fulfill that specific requirement. The other course options cannot be used to satisfy that same requirement or any other major requirements.
  • Senior Project (8 units): DSC 180A-B
  • Electives (20 units):
    • Any upper-division DSC course NOT listed as a core requirement and NOT used to fulfill other requirements.
    • Any of the approved electives at the bottom of the page.
    • DSC courses that do not count toward major requirements: DSC 197, 198, and 199.

Notes on Electives:

  1. Students will be expected to fulfill all prerequisites for all courses, which may entail additional coursework beyond the DSC major requirements.
  2. Students may petition to satisfy up to 8 elective units using upper-division courses not on the list above but in their subject domain.
  3. A maximum of 12 units of courses offered with only a P/NP grade option may be taken. Courses with a letter grade option must be completed for a letter grade.

Entered UCSD Prior to Fall 2020

Students who entered UCSD before the 2020-2021 academic year are allowed to follow our Transition Plan, which accommodates students who are interested in meeting major requirements between the old curriculum and our new curriculum. Students who have questions or concerns regarding these accommodations should contact DSC Student Affairs immediately through the Virtual Advising Center.

Students who entered UCSD prior to FA20 can choose 1 of the 3 options:

  • Option 1: Strictly follow the old requirements as presented in the Academic Catalog when they entered the major. This Academic Catalog is reflected in the individual student’s Degree Audit.
  • Option 2: Follow only the new requirements as presented in the 2020-2021 Academic Catalog. These students must notify DSC Student Affairs through their Virtual Advising Center for their Degree Audit to be updated to the 2020-2021 Academic Catalog.
  • Option 3: Pursue a combination of the old requirements and the new requirements with the following flexibility:
    • Students are allowed to complete the new lower-division subject domain options to facilitate meeting prerequisites for electives.
    • Students are allowed to pursue elective courses from the combined list of elective courses from the Academic Catalog of their entering year and from the new requirements.
    • Students who choose to follow Option 3 are required to notify DSC Student Affairs through their Virtual Advising Center for each individual Degree Audit update.

Elective Recommendations by Domain Track

We have created the following 4 initial tracks within the undergraduate DSC major curriculum:

  1. Science
  2. Social Science
  3. Business Analytics and Econometrics
  4. Machine Learning and Artificial Intelligence

These tracks broadly cover the breadth of usage of data science in research and industry. The tracks will guide students to a particular subfield of data science, while making the content of the major more easily understandable by industry managers hiring our graduates.

Lower Division

Students must choose 1 of the following 2-course sequences (8 units):

Science: BILD 1 and BILD 3

  • BILD 1: The Cell
  • BILD 3: Organismic and Evolutionary Biology

Social Science: (POLI 5 and POLI 30) or (SOCI 60 and USP 4)

  • POLI 5: Data Analytics for the Social Sciences
  • POLI 30: Political Inquiry
  • SOCI 60: The Practice of Social Research
  • USP 4: Introduction to Geographic Information Systems

Business Analytics, Econometrics, and Statistics: ECON 1 and ECON 3

  • ECON 1: Principles of Microeconomics
  • ECON 3: Principles of Macroeconomics

Machine Learning and Artificial Intelligence: COGS 14A and COGS 14B

  • COGS 14A: Introduction to Research Methods
  • COGS 14B: Introduction to Statistical Analysis

Upper Division

All majors must complete 20 units of upper-division Domain of Interest electives.

A “Domain of Interest” refers to any field where a student is interested in applying their data science knowledge and skill set. Data science is applicable to many fields, so it is important for students to critically consider how they want to pursue a career in data science. The goal is to utilize electives to build a depth of knowledge within the student’s desired domain. Therefore, students are strongly recommended to pursue the following electives based on their chosen lower-division track.

That said, students can complete any listed elective below if they are interested in multiple domains and/or skills and knowledge available through electives within different recommended tracks as long as they satisfy the necessary prerequisites. In other words, students are NOT required to follow the same upper-division track based on their lower-division track. Students only need to complete 1 lower-division track to meet requirements, but may be required to complete additional lower-division courses to meet prerequisites for electives outside of the recommended track for the chosen lower-division sequence.

Students may petition for up to 8 units of upper-division courses that are related to their “Domain of Interest” to fulfill their DSC electives. You can find more information on the Petition Instructions page of our website.

  • BICD 100: Genetics
  • BIEB 174: Ecosystems and Global Change
  • SIO 132: Introduction to Marine Biology
  • SIO 109(R): Bending the Curve: Climate Change Solutions*
  • POLI 117(R): Bending the Curve: Climate Change Solutions*
  • ESYS 103: Environmental Challenges: Science and Solutions*
  • MAE 124: Environmental Challenges: Science and Solutions*
  • COGS 180: Decision Making in the Brain
  • CSE 180: Biology Meets Computing
  • PSYC 106: Behavioral Neuroscience
  • COMM 106I: Internet Industry
  • PHIL 174: Data Ethics
  • POLI 170A: Applied Data Analysis for Political Science
  • POLI 171: Making Policy with Data
  • POLI 172: Advanced Social Data Analytics
  • POLI 173: Social Network Analysis
  • SOCI 102: Network Data and Methods
  • SOCI 103M: Computer Applications to Data Management in Sociology
  • SOCI 108: Survey Research Design
  • SOCI 109: Analysis of Sociological Data
  • SOCI 109M: Research Reporting
  • SOCI 136: Data and Society
  • SOCI 165: Predicting the Future from Tarot Cards to Computer Algorithms
  • SOCI 171: Technology and Science
  • USP 122: Redevelopment Planning, Policymaking, and Law
  • USP 125: The Design of Social Research
  • USP 138: Urban Economic Development
  • USP 153: Real Estate and Development Market Analysis
  • USP 172: Graphics, Visual Communication, and Urban Information
  • USP 175: Site Analysis: Opportunities and Constraints
  • USP 180: Transportation Planning
  • ECON 120B: Econometrics B
  • ECON 120C: Econometrics C
  • MATH 152: Applicable Mathematics and Computing
  • MATH 173A: Optimization Methods for Data Science I
  • MATH 173B: Optimization Methods for Data Science II
  • MATH 180A: Introduction to Probability
  • MATH 180B: Introduction to Stochastic Processes I
  • MATH 180C: Introduction to Stochastic Processes II
  • MATH 181A: Introduction to Mathematical Statistics I
  • MATH 181B: Introduction to Mathematical Statistics II
  • MATH 181C: Mathematical Statistics—Nonparametric Statistics
  • MATH 181D: Statistical Learning
  • MATH 181E: Mathematical Statistics—Time Series
  • MATH 181F: Sampling Surveys and Experimental Design
  • MATH 194: The Mathematics of Finance
  • MGT 103: Product Marketing and Management
  • MGT 153: Information Technology in Business Analytics
  • COGS 108: Data Science in Practice
  • COGS 109: Modeling and Data Analysis
  • COGS 118C: Neural Signal Processing
  • COGS 118D: Mathematical Statistics for Behavioral Data Analysis
  • COGS 120: Interaction Design*
  • CSE 170: Interaction Design*
  • COGS 121: Human Computer Interaction Portfolio Design Studio
  • COGS 181: Neural Networks and Deep Learning*
  • CSE 151B: Deep Learning*
  • COGS 189: Brain Computer Interfaces
  • CSE 106: Discrete and Continuous Optimization
  • CSE 152A: Introduction to Computer Vision I
  • CSE 152B: Introduction to Computer Vision II
  • CSE 156: Statistical Natural Language Processing
  • CSE 166: Image Processing
  • LIGN 167: Deep Learning for Natural Language Understanding

Note: Courses with * are cross-listed. Students can complete the course through either department; however, students must meet prerequisites as listed on the Academic Catalog.