Major Requirements

Major Requirements

Entered UCSD Fall 2020 and thereafter

The major consists of 112 units with fifty-two units from lower-division courses and sixty units from upper-division courses. The lower-division curriculum includes calculus and linear algebra courses for sixteen units, data science courses for twenty-eight units, and subject domain courses for eight units. 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-two 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, DSC 10, DSC 20, DSC 30, DSC 40A-B, DSC 80 (twenty-eight units)
  • Mathematics: MATH 18 or MATH 31AH, MATH 20A-B-C or MATH 31BH (sixteen units)
  • Subject Domain Courses: Students must choose one of the following two-course sequences (eight units)
    • 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 or ECON 5) and POLI 30 
    • Social Sciences II: SOCI 60 and USP 4


Students must complete sixty upper-division units. All courses must be taken for a letter grade unless offered Pass/No Pass only. A minimum grade of C– is required.

  1. Core Courses (thirty-two units): ECON 120A or MATH 183 or MATH 181A, MATH 189, DSC 100, DSC 102, DSC 106, CSE 150A or DSC 140A, CSE 151A, CSE 158
  2. Senior Project (eight units): DSC 180A-B
  3. Electives (twenty units)
    • Any upper-division data science course not used to fulfill other requirements with the exception of DSC 197, 198, and 199.
    • Any of the following: BICD 100 and BIEB 174; COGS 108, 109, 118C-D, 120 (cross-listed with CSE 170), 121, 180, 181, and 189; CSE 106,151B, 152A, 152B, 156, 166, 170 (cross-listed with COGS 120) and 180; ECON 120B and 120C; ESYS 103 (cross-listed with MAE 124); LIGN 167; MAE 124 (cross-listed with ESYS 103); MATH 152, 173A-B, 180A-B-C, and 181A-B-C-D-E-F, and 194; MGT 103 and 153; POLI 117 (cross-listed with SIO 109), 137, 170A, 171, 172, and 173; PSYCH 106; SIO 109 (cross-listed with POLI 117) and 132; SOCI 102, 103M, 108, 109, 109M, 136, 165, and 171; USP 122, 125, 138, 153, 172, 175, and 180.
    • Students may petition to satisfy up to eight elective units using upper-division courses not on the list above but in their subject domain.
    • Students will be expected to fulfill all prerequisites for all courses, which may entail additional coursework beyond the data science major requirements.
    • A maximum of twelve units of courses offered with only a Pass/No Pass 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 Academic Year of 2020-2021 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 the Undergraduate Advisor immediately through the Virtual Advising Center.

Students who entered UCSD prior to Fall 2020 can choose one of the three options:  

  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.
  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.
  3. Pursue a combination of the old requirements and the new requirements with the following flexibility:
    1. Students are allowed to complete the new lower division subject domain options to facilitate meeting prerequisites for electives.
    2. Students are allowed to pursue elective courses from the combined list of elective courses from their original academic year catalog and from the new requirements.

i. 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 needed.

Elective Recommendations by Domain Track

We have created the following four initial tracks within the undergraduate Data Science 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. They will guide students to a particular flavor of Data Science, while making the content of the major more easily understandable by industry managers hiring our graduates. 


Students must choose one of the following two-course sequences (eight 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 IntelligenceCOGS 14A and COGS 14B

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


All majors must complete 20 units of upper division electives. 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 the student is interested in multiple domains and/or skills and knowledge available through electives within different recommended tracks. Students will be expected to complete all listed prerequisites. Thus, the student only needs to complete one 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.

Science Domain Track Electives

  • BICD 100: Genetics 
  • BIEB 174: Ecosystems and Global Change
  • SIO 132: Introduction to Marine Biology
  • SIO 109:Bending the Curve: Climate Change Solutions*
  • POLI 117: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
  • PSYCH 106: Behavioral Neuroscience

Social Sciences Track Electives

  • POLI 137: A Sports Analytics Approach to the Social Sciences 
  • 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

Business Analytics, Econometrics, and Statistics Track Electives

  • 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: Business Analytics

Machine Learning and Artificial Intelligence Track Electives

  • 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 
  • CSE 170: Interaction Design*
  • COGS 120: Interaction Design* 
  • COGS 121: Human Computer Interaction Programming Studio 
  • COGS 181:Neural Networks and Deep Learning
  • COGS 189: Brain Computer Interfaces 
  • CSE 106: Discrete and Continuous Optimization
  • CSE 151B:Deep Learning
  • CSE 152A: Introduction to Computer Vision 
  • 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.