University of California, Los Angeles

Masters of Applied Statistics and Data Science (2024-2026)

Course Schedule for UCLA

Intro to Probability Models

Grade:


Introduction to probability theory, probability models, and stochastic processes, with emphasis on concepts, intuitions, calculations, and real applications

Survey of Methods in Modern Statistics

Grade:


Overview of fundamental concepts of data analysis and statistical inference and how these are applied in wide variety of settings. Arc of statistical investigation, including data collection, data exploration, formal inference, and model checking

Applied Regression

Grade:


Introduction to state-of-art applications of linear model for understanding systems and predicting outcomes. Topics include review of statistical inference, properties of least-squares estimates, interpreting linear model, prediction and confidence intervals, model building, diagnostics, and bootstrapping

Mathematical Statistics

Grade:


Mathematics used to prove various statistical theories, with emphasis on real-world applications. Estimation and statistical inference. Random variables and their distributions; random vectors, their means, variances, variance covariance matrix; and important limit theorems such as central limit theorem

Statistical Computing and Programming

Grade:


Fundamentals of statistical programming using R, C, and C++. Statistical applications involve linear and nonlinear regression, shrinkage methods, density estimation, numerical optimization, maximum likelihood estimation, classification, and resampling. Graphics and real examples used to illustrate techniques. Analyses of both real and simulated data.

Data Management

Grade:


Introduction to and use of variety of software and languages, such as Python, SQL, Stata, SAS, R. Basic principles of data management, including reading and writing various forms of data, working with databases, data cleaning, validation, transformation, exploratory data analysis, and introductory data visualization and data mining techniques. Exploration of related issues of data security, ethics, and scalability

Advanced Statistical Communication

Grade:


Designed to improve verbal and written communication skills related to various ways in which statistics in used in workplace

Machine Learning

Grade:


Introduction to machine learning and data mining methods. To gain in-depth understanding of these methods, implementation of them in R, Python, and C++