The Master of Arts (M.A.) in Statistics at UC Santa Barbara prepares students for careers in a wide variety of fields, including insurance, pharmaceuticals, consulting, government, and data science.  

Program Overview

Students complete classwork designed to prepare them to use modern methods in applied statistics with understanding and rigor. The required courses in Statistical Methods and Data Analysis are the same ones that our Doctoral students complete. The program prepares students to consult on real data projects and communicate their analysis to colleagues.

Data Science Specialization

This specialization integrates modern statistical methodologies with computational approaches for analyzing big data. It prepares students for careers in data science and analytics, emphasizing tools and techniques for handling large datasets.

Admission Requirements

Applicants must hold a bachelor's degree in Statistics, Mathematics, or a related quantitative field.

Successful applicants generally have completed coursework in math and statistics covering 

  • Calculus-based probability
  • Statistical inference - estimation and hypothesis testing
  • Regression modelling
  • Statistical computing
  • Linear algebra

How To Apply Page

Degree Requirements

General Requirements

Students must complete 42 units of coursework with an average GPA of 3.0. Each core course must be completed with a grade of B or better. Where possible, all courses should be taken for a letter grade.
Students must complete a data analysis project report. This is usually completed as part of the work in PSTAT 230 on specific consulting projects.

Course Requirements

Applied Statistics

  • Core Courses:
    • PSTAT 220 A-B-C: Advanced Statistical Methods
    • PSTAT 122: Design and Analysis of Experiments
    • PSTAT 230: Seminar and Projects in Statistical Consulting

Data Science Specialization

  • Core Courses:
    • PSTAT 220 A-B-C: Advanced Statistical Methods
    • PSTAT 230: Seminar and Projects in Statistical Consulting
    • PSTAT 234: Statistical Data Science
  • Restricted Electives: Students must complete at least two of the following:
    • PSTAT 215A: Bayesian Inference
    • PSTAT 231: Data Mining
    • PSTAT 232: Computational Techniques in Statistics
    • PSTAT 235: Big Data Analytics
    • PSTAT 237: Uncertainty Quantification

PhD Students

Students enrolled in our PhD program who do not already have an MA in Statistics are eligible to petition for the degree once they have

  • Passed two of the three core sequences (PSTAT 207ABC, 213ABC, or 220ABC)
  • Passed the Qualifying Exam in Statistics or Probability at the Masters level.
  • Completed a total of 42 units of graduate level coursework at UCSB.

Students from other Departments


PhD students from other UCSB Departments are eligible to work towards the MA in Applied Statistics. 

Before deciding whether to pursue the additional degree objective, we recommend that you take some of the courses in the Statistics Department. In particular, we recommend PSTAT 122, PSTAT 127,  PSTAT 220ABC, PSTAT 234, and PSTAT 231.

Courses taken to fulfill requirements for your PhD degree in another department cannot be used to count towards the MA Statistics degree. 

You can set up an appointment with one of the Graduate Advisors to discuss the details of the requirements in your specific circumstances.

Contact studentaffairs@pstat.ucsb.edu for information about petitioning to add the Statistics degree objective.

Time to Degree Expectations

The University has asked every department to establish normative times for degree completion. These are expectations based on students with the typical preparation.

The Department expects students to be able to complete the MA in Applied Statistics in 2 years.

However, the department acknowledges that students come to our program with diverse backgrounds, training, and strengths. We work with all students to establish an academic plan that works for them. 

Career Opportunities

Graduates of the M.A. program in Statistics are well-prepared for careers in:

  • Data Science and Analytics
  • Statistical Consulting
  • Government and Public Policy
  • Insurance and Risk Management
  • Pharmaceuticals and Biostatistics

Resources

Progress Sheets