Master of Applied Statistics and Data Science

UCLA's Master of Applied Statistics and Data Science is a professionally oriented graduate programme designed for students and working professionals seeking advanced expertise in statistical modelling, data analysis, programming, and data management. The programme combines rigorous statistical training with practical applications, industry-connected thesis research, and flexible evening classes, enabling students to develop analytical and computational skills for solving real-world problems across technology, finance, healthcare, research, and business.

Curriculum Structure

The programme typically takes six academic quarters to complete. UCLA does not prescribe a fixed year-by-year course sequence, so the following is an indicative progression based on its official core courses, electives, and thesis requirements.

Year 1: Statistical Foundations and Applied Data Science

During the first year, students establish a strong foundation in probability, statistical theory, regression analysis, and computational methods. Core modules include Introduction to Probability Models (Statistics 400), Survey of Methods in Modern Statistics (Statistics 401), Applied Regression (Statistics 402), and Mathematical Statistics (Statistics 403). Students also develop practical programming and data-handling abilities through Statistical Computing and Programming (Statistics 404) and Data Management (Statistics 405), learning to organize, analyse, and interpret complex datasets.

Year 2: Advanced Statistical Communication, Specialization, and Research

During the second year, students strengthen their analytical and professional communication skills through Advanced Statistical Communication (Statistics 421B) and select four elective courses covering advanced statistical modelling and programming. Depending on their interests, students can explore subjects such as pattern recognition and machine learning, multivariate analysis, causality, forecasting, deep learning, large language models, and Bayesian statistics. Students also undertake an original thesis project under the supervision of a UCLA faculty adviser, addressing a real-world analytical problem using statistical methods and, where available, data and collaboration from an industry partner.

Focus Areas

• Applied Statistics and Data Science

• Statistical Modelling and Inference

• Probability and Distribution Theory

• Applied Regression Analysis

• Statistical Computing and Programming

• Data Management and Database Systems

• Machine Learning and Pattern Recognition

• Deep Learning and Artificial Intelligence

• Bayesian Statistics

• Multivariate Statistical Analysis

• Causal Inference

• Forecasting and Predictive Analytics

• Large Language Models

• Statistical Research and Data Communication

Learning Outcomes

Graduates of this programme will be able to:

• Apply advanced statistical methods to analyse complex real-world datasets.

• Develop statistical models for prediction, estimation, and decision-making.

• Use programming languages such as R and Python for statistical computing and data analysis.

• Apply probability theory and statistical inference to solve analytical problems.

• Perform regression analysis and evaluate relationships between different variables.

• Manage, organize, and process large datasets using appropriate data management techniques.

• Apply machine learning and advanced statistical modelling methods.

• Interpret research findings and communicate statistical results effectively.

• Design independent research projects and develop solutions to practical data-related problems.

• Collaborate with academic researchers and industry professionals on applied statistical investigations.

Professional Alignment (Accreditation)

The official university programme information does not specify a separate professional accreditation for the Master of Applied Statistics and Data Science. However, the curriculum provides professional preparation through advanced statistical coursework, programming, data management, and supervised thesis research. Students can also pursue an approved internship and earn up to four units of academic credit by completing a substantial research paper connected to their internship experience.

Reputation (Employability Rankings)

• Industry Connections: UCLA encourages students to participate in internships and provides opportunities to connect with industry partners through thesis projects and professional networking activities.

• Practical Research Experience: Students complete an original thesis addressing a real-world problem, potentially using datasets and research topics provided by industry partners.

• Professional Flexibility: Evening classes and flexible course loads allow working professionals to pursue graduate education while continuing their careers.

• Career Preparation: The programme develops skills relevant to careers in statistical analysis, data science, quantitative research, predictive modelling, and business analytics.

• Employment Statistics: The university does not publish a specific programme-level graduate employment rate, median salary, or QS/Guardian employability ranking for this degree.