teaching at UCLA

STATS 10: Introduction to Statistical Reasoning

Taught in Summer 2019, Winter and Fall 2023, Winter and Spring 2024. Lecture, three hours; discussion, one hour; computer laboratory, two hours. Preparation: three years of high school mathematics. Not open for credit to students with credit for course 12, 13, or 15. Introduction to statistical thinking and understanding, including strengths and limitations of basic experimental designs, graphical and numerical summaries of data, inference, regression as descriptive tool. P/NP or letter grading.

STATS 20: Introduction to Statistical Programming with R

Taught in Fall 2024. Lecture, three hours; discussion, one hour. Enforced requisite: course 10, 12, or 13. Designed to prepare students for upper-division work in statistics. Introduction to use of R, including data management, simple programming, and statistical graphics in R. P/NP or letter grading.

STATS 102A: Introduction to Computational Statistics with R

Taught in Summer 2024. Lecture, three hours; discussion, one hour. Requisites: course 20, Mathematics 33A, and one course from course 10, 12, 13, Economics 11, 41, or Psychology 100A, or score of 4 or higher on Advanced Placement Statistics Examination. Introduction to computational statistics through numerical methods and computationally intensive methods for statistical problems. Topics include statistical graphics, root finding, simulation, randomization testing, and bootstrapping. Covers intermediate to advanced programming with R. P/NP or letter grading.

M 148: Introduction to Data Science

Taught in Winter 2024. Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: one course from 131A, Civil and Environmental Engineering 110, Mathematics 170A, Mathematics 170E, or Statistics 100A, and Computer Science 31 or Program in Computing 10A, and 10B. How to analyze data arising in real world so as to understand corresponding phenomenon. Covers topics in machine learning, data analytics, and statistical modeling classically employed for prediction. Comprehensive, hands-on overview of data science domain by blending theoretical and practical instruction. Data science lifecycle: data selection and cleaning, feature engineering, model selection, and prediction methodologies. Letter grading.