Data Theory BS

Capstone Major

Learning Outcomes

The Data Theory major has the following learning outcomes:

  • Understanding of mathematical and statistical bases of most common methods of data science
  • Ability to explain in writing, with examples, how concepts of statistics and mathematics together solve real-world problems involving data
  • Skillfully manage data
  • Development, comparison, and testing of data-driven models to solve problems
  • Understanding and explanation of variability when fitting and interpreting models of real-world systems
  • Carrying out of reproducible data analysis using accepted practices of research community
  • Written and verbal communication of findings of analyses
  • Identification of areas of active research in data science
  • Insightfully address problems concerning ethics of data use and storage, including data privacy and security
  • Demonstrated mastery of concepts and skills of machine learning, modeling and supervised learning, dimension reduction and unsupervised learning, and deep learning
  • Demonstrated familiarity with numerous software tools used in statistical and data science work and research
  • Demonstrated knowledge of mathematical foundations, including pure and applied linear algebra, basic analysis, probability, and optimization theory
  • Study and evaluation of proofs of mathematical and statistical results employed in data theory
  • Work effectively in a team on a data science problem
  • Demonstrated eligibility for graduate study in applied mathematical science or statistical science


Students entering UCLA directly from high school or first-term transfer students who want to declare the Data Theory premajor at the time they apply for admission are automatically admitted to the premajor. Students must visit the student services office of either the Mathematics Department or Statistics Department in order to petition to enter the major. All students are identified as Data Theory premajors until they satisfy the following minimum requirements for the major.

Preparation for the Major

Required: Mathematics 31A, 31B, 32A, 32B, 33A, 42, 115A; Program in Computing 10A; one course selected from Statistics 10, 12, 13, 15; Statistics 20, 21. Each course must be completed with a grade of C or better and an overall grade-point average of at least 2.7. All students must take Mathematics 42 at UCLA. The major is limited in size according to available resources.

Repetition of more than two mathematics or statistics sequenced courses or of any mathematics or statistics sequenced course more than once results in automatic dismissal from the major.

Freshman Students

To enter the major, students must petition after they have completed the preparation for the major courses. Students who have an overall grade-point average (GPA) of at least 3.3 in the preparation for the major courses, and have completed all preparation for the major courses before the fall quarter of their third year at UCLA, will be admitted to the major.

Students whose overall GPA is between 2.7 and 3.3, or who fail to complete the preparation courses before the fall quarter of their third year, are admitted only if space is available. All students must petition before they have earned 160 units, or by the winter quarter of their junior year, whichever comes first. Only grades for courses that are taken at the University of California, including UC summer schools, are counted for this GPA computation.

Transfer Students

Transfer applicants to the Data Theory major are admitted to the premajor. Applicants with 90 or more units must have completed the following by the end of the spring term prior to entry to UCLA: two years of calculus for physical science and/or engineering majors, one linear algebra course, one C++ programming course, one statistics course.

Transfer students must have completed all preparation for the major coursework, and must have passed Mathematics 42, 115A, and at least 4 units of upper-division coursework required for this major with at least a 3.3 GPA, in order to be eligible to petition to enter the major. Transfer students will be admitted to the major if they satisfy these requirements. Transfer students who fail to meet these criteria for automatic admission will be admitted only if resources allow. Transfer students must petition to enter the major no later than the spring quarter of their first year at UCLA.

Refer to the UCLA transfer admission guide for up-to-date information regarding transfer selection for admission.

After satisfying the preparation for the major requirements, students must visit the student services office of either the Mathematics Department or Statistics Department in order to petition to enter the major.

The Major

Required: Mathematics 118, 131A, 156, Statistics 101A, 102A, 102B, 101C, 147, 184; one two-quarter sequence: Mathematics 170E and 170S, or Statistics 100A and 100B; one elective selected from Mathematics 151A, 151B, 164, 168, 171, 174E, 178A, 178B, 178C, 179 or 182; one elective selected from Statistics 100C, 101B, 102C, or C151 through 199 (except Statistics 182, 186, or 189); two additional electives from either of the above lists; a capstone course (Mathematics M148 or Statistics M148), to be taken during the final year.

Only 4 units of course 199 may be applied toward the major. Courses 189 and 189HC may not be applied toward any of the major requirements.

Each major course must be taken for a letter grade, and students must have an overall grade-point average of 2.0 or better.