Requisite: course 100A. Requisite: course 100A or Mathematics 170A or 170E. Popular courses include pre-calculus, differential and integral calculus, statistics, and integration and infinite series. Recommended requisites: courses 208, M231A. Statistical techniques in investment theory using real market data. Limited to 20 students. Individual contract required. Research group meeting, two hours; fieldwork, two hours. Requisites: Mathematics 32B, 33A. Statistics Minor Worksheet There are two documents available below that are helpful to UCLA Statistics minors. S/U grading. Examples provided throughout, and students implement techniques discussed. Importance and rejection sampling. Such gigantic volumes of data produced cannot be analyzed and understood without highly sophisticated computational methods guided by mathematical and statistical principles. The UCLA General Catalog is published annually in PDF and HTML formats. Lecture, three hours. P/NP or letter grading. Principal components, canonical correlation, discriminant analysis. Graphics and real examples used to illustrate techniques. Reviews, ratings and grades for STATS 10 - Introduction to Statistical Reasoning | Bruinwalk is your guide to the best professors, courses and apartments in UCLA. Skills developed apply to any discipline in which investigators seeks to make causal statements but cannot fully randomize treatment. Students meet on regular basis with instructor and provide periodic reports of their experience. Introduction to statistical thinking and understanding, with emphasis on techniques used in geography and environmental science. Take courses that don’t repeat material you have already completed. Lecture, three hours. Simulated annealing. Principles of deductive logic and causal logic using counterfactuals. R; Stata; SAS; SPSS; Mplus; Other Packages. Seminar, two hours. Not open for credit to students with credit for course 11, 12, 13, 14, or former course 10H. Requisites: courses 100B, 102A. Mathematics used to prove various statistical theories, with emphasis on real-world applications. Courses are the equivalent undergraduate curricula offered at UCLA. Introduction to Statistics and Quantitative Methods STATS X 402 This introductory statistics course emphasizes practical application of statistical analysis. Practical and theoretical issues in teaching of statistics. P/NP or letter grading. Introduction to analysis of social structure, conceived in terms of social relationships. Lecture, three hours; discussion, one hour. Lecture, three hours; discussion, one hour. Exploration of broader regression/classification techniques that have been ubiquitous in machine learning literature, with special attention to regularization and kernelized methods. Lecture, three hours; discussion, one hour. Requisite: course 100A or Electrical Engineering 131A or Mathematics 170A. P/NP or letter grading. Concepts and methods tailored for analysis of epidemiologic data, with emphasis on tabular and graphical techniques. Limited to graduate students. Preparation: three years of high school mathematics. Letter grading. Concurrently scheduled with course C261. Limited to Master of Applied Statistics students. Lecture, three hours; discussion, one hour. Lecture, four hours. Exposure to several statistical techniques used in investment theory, and hands-on experience by applying various models on real stock market data using package stockPortfolio of open source statistical software R. Letter grading. S/U grading. Statistical techniques in investment theory using real market data. Limited to students in College Honors Program. Lecture, three hours; discussion, one hour. Likelihood ratio test, p-value, false discovery, nonparametrics, semi-parametrics, model selection, dimension reduction. Markov chain Monte Carlo (MCMC) sampling techniques, with emphasis on Gibbs samplers and Metropolis/Hastings. Probability and statistics topics in data-driven and interactive manner using open Internet resources. Individual honors contract required. Designed for graduate students. Lecture, three hours. Exact sampling with coupling from past. Recommended: course 102A. We have also partnered with the Mathematics department to offer Data Theory , a new major at UCLA. Recommended preparation: programming skills in R, C/C++, MATLAB. Letter grading. Letter grading. Performance of analyses of real-world datasets. Use of Statistics Online Computational Resource (SOCR). Lecture, three hours; discussion, one hour. Limited to Master of Applied Statistics students. Preparation: basic knowledge of calculus, linear algebra, and computer programming. Focus on programming with Python and selection of its libraries: NumPy, pandas, matplotlib, and scikit-learn, for purpose of data processing, data cleaning, data analysis, and machine learning. Computer implementation. Department of Statistics, University of California, Los Angeles. Concurrently scheduled with course C116. P/NP or letter grading. Course Description. Letter grading. Recommended: some experience in statistical computing. Lecture, three hours. Letter grading. Limited to Master of Applied Statistics students. (Same as Geography M205 and Urban Planning M215.) Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Use of computer programming to solve statistical problems. Lecture, three hours; discussion, one hour. Seminar, two hours. UCLA General Catalog 2020-21. Concurrently scheduled with course C225. Not open to students with credit for Electrical Engineering 131A or Mathematics 170A; open to graduate students. Requisite: course 100B or Mathematics 33A. Tutorial, one hour. Examination of computer simulation in depth and discussion of computational approximations of solutions to complex problems using R, with examples of situations and concepts that arise naturally when playing Texas Hold'em and other games. It replaces the traditional formula-based approach to statistics with an emphasis on computer simulation of chance probabilities. Lecture, three hours; discussion, one hour. We also help organize the annual ASA DataFest — the largest data hackathon on the West Coast. Students should consult their respective counseling office to determine which courses best fulfill their GE requirements. Letter grading. Students present statistical results for audiences ranging from business leaders to media outlets to academic statisticians. They are the major class planning worksheet, the major contract, the course roadmap, the prerequisites checklist and the form to add major / minor at 150+ units. P/NP or letter grading. (Same as Bioinformatics M223 and Biomathematics M271.) Lecture, three hours. Required of all potential departmental teaching assistants and new PhD students. Exploration of methods used in analysis of numerical time-series data. Concurrently scheduled with course C155. P/NP or letter grading. P/NP grading. The course covers the role of statistics in the fields of science, economics, nursing, business, and medical research. Basic principles, ANOVA block designs, factorial designs, unequal probability sampling, regression estimation, stratified sampling, and cluster sampling. Seminar, three hours. Lecture, three hours; discussion, one hour. Recommended requisite: course 200B. Letter grading. Lecture, three hours. Lecture, three hours; discussion, one hour. UCLA has a rich history of athletics but sometimes love finds its way into the UCLA sporting world. Prepares students for applied project work. Practical applications of sampling methods via lectures and hands-on laboratory exercises. Limited to Master of Applied Statistics students. S/U or letter grading. Large Sample Theory, Including Resampling (4) 10. Analyses of both real and simulated data. Lecture, three hours; discussion, one hour. New York: Springer. Lecture, three hours; discussion, one hour. Simpson paradox and confounding control. Introduction to theoretical analysis of machine learning methods, with emphasis on prediction problems. S/U or letter grading. Lecture, three hours; discussion, one hour. Teaching apprenticeship under active guidance and supervision of regular faculty member responsible for curriculum and instruction at UCLA. Selected theories for quantification of psychological, educational, social, and behavioral science data. Introduction to data science, including data management, data modeling, data visualization, communication of findings, and reproducible work. Lecture, three hours; discussion, one hour. GE regulations and application of GE credit vary among the College and schools. Covers use of text mining tools for purpose of data analysis. May be repeated for maximum of 4 units. Limited to junior/senior USIE facilitators. Principles of probability logic and probabilistic induction. Introduction to advanced topics in statistical modeling and inference, including Bayesian hierarchical models, missing data problems, mixture modeling, additive modeling, hidden Markov models, and Bayesian networks. Lecture, three hours; discussion, one hour. Lecture, three hours; discussion, one hour. P/NP or letter grading. Requisites: courses 100B, 102A, Mathematics 33A. To search courses, enter keyword(s) in the field and click the search button. S/U grading. Applications. Designed for graduate students. Lecture, three hours; discussion, one hour. CCPR Traineeships; Donald J. Treiman Research Fellowship; Seed Grant Program; Course Release Program; External Sources; Funding Opportunities; Other Funding Opportunities; NIH Information; NSF Information; Working Papers. Foundation of basic concepts and techniques of statistics. Rick Paik-Schoenberg, Jan de Leeuw and Mark Handcock, the three former Chairs of our department, pose for a photo at the UCLA Statistics 20th anniversary event on … Participation in oral presentations of student work. Emphasis on non-asymptotic bounds via concentration inequalities. Monte Carlo methods and numerical integration. Reasonable level of competence in both statistics and computing is required. Study of fundamental methods in data visualization, focusing especially on methods using Tableau, R/shiny, Python/Dash and HTML, and JavaScript for customized R and Python dashboards. Lecture, four hours. Letter grading. Letter grading. Search this website. We have also partnered with the Mathematics department to offer. P/NP or letter grading. S/U or letter grading. Study of four types of statistical models for modeling visual patterns: descriptive, causal Markov, generative (hidden Markov), and discriminative. Lecture, three hours. Email: An-drew.Forney@lmu.edu zDepartment of Computer Science, University of California, Los Angeles. Fundamental concepts, theories, and algorithms for pattern recognition and machine learning that are used in computer vision, image processing, speech recognition, data mining, statistics, and computational biology. Limited to students in College Honors Program. UCLA Extension courses are widely recognized and respected for their rigorous and research-informed academic content. Recommended requisites: courses M232A, M232B. Limited to Master of Applied Statistics students. Theory and modern methods for analyzing both lattice and point process data using R, and student performances of their own analysis of geostatistical datasets involving variogram modeling, kriging, model fitting, and estimation using maximum likelihood and nonparametric methods. Formulation of decision making problem as probabilistic inference. S/U or letter grading. Lecture, three hours; discussion, one hour; laboratory, one hour. Limited to Master of Applied Statistics students. For undergraduate students a broad range of courses covering applications, computation, and theory is offered. Survey of modern methods used in analysis of spatial data. Expansion of topics introduced in Epidemiology 200B and 200C and introduction of new topics, including principles of epidemiologic analysis, trend analysis, smoothing and sensitivity analysis. Honors content noted on transcript. Probabilities of counterfactuals. Concurrently scheduled with course C151. Tutorial, four hours. Recommended to be taken prior to or concurrently with course M148. Limited to juniors/seniors. Topics include sampling distributions, statistical estimation (including maximum likelihood estimation), statistical intervals, and hypothesis testing, with emphasis on application of these concepts. Every effort has been made to ensure the accuracy of the information presented in the UCLA General Catalog.However, all courses, course descriptions, instructor designations, curricular degree requirements, and fees described herein are subject to change or deletion without notice. P/NP or letter grading. Recommended requisite: Epidemiology 200C. Students interested in the Statistics minor should meet with the student affairs officer early in their careers. Other technologies covered include Jupyter notebook, Structured Query Language (SQL), and git. Lecture, three hours; discussion, one hour. Statistics Lower-Division Courses. May be repeated for credit with permission from program chair or instructor. S/U or letter grading. (Same as Epidemiology M211.) Lecture, three hours. Letter grading. Lecture, three hours; discussion, one hour. Topics include role of randomization and blocking, comparing two or more treatments, randomized blocks, factorial design, Latin square designs, fractional factorial designs, response surface designs. Springer series in statistics. Examples of applications vary according to interests of students. Introduction to Markov chain Monte Carlo (MCMC) algorithms for scientific computing. Draws upon relevant examples in scientific literature. Introduction to statistical inference based on use of Bayes theorem, covering foundational aspects, current applications, and computational issues. P/NP or letter grading. Recommended preparation: linear algebra, calculus, basic computer programming knowledge. Recommended requisite: course 202A. Topics in various statistical areas by means of lectures and informal conferences with staff members. S/U or letter grading. P/NP or letter grading. Individual study in regularly scheduled meetings with faculty mentor while facilitating USIE 88S course. Lecture, three hours; discussion, one hour. Introduction to state-of-art statistical methods that rely on historical data collected in past to forecast future outcomes. Not open to students with credit for course 10, 12, 13, or former course 10H, 11, or 14. May not be repeated. Reasonable level of competence in both statistics and mathematics required. Requisites: courses 10, 20, 101A, or equivalent level of discipline. Limited to Master of Applied Statistics students. (Same as Psychology M144.) Students work in small groups with faculty member and client to frame client's question in statistical terms, create statistical model, analyze data, and report results. Focus on what is done when linear models are not appropriate and may produce misleading estimates. Asymptotic properties of tests and estimates, consistency and efficiency, likelihood ratio tests, chi-squared tests. Lecture, three hours; discussion, one hour. Exploration of related issues of data security, ethics, and scalability. Limited to seniors. Statistics Course Lab Datasets (from the UCLA Department of Statistics) Statistics Labs (from the UCLA Department of Statistics) Electronic Dataset Service; The Data and Story Library (DASL) StatLib Dataset Archive (from the CMU Department of Statistics) Web Based Textbooks. Lecture, four hours; discussion, two hours. Seminar, one hour; discussion, one hour; research group meeting, two hours. Lecture, three hours; discussion, one hour. Identifying causes of events. Statistical applications involve linear and nonlinear regression, shrinkage methods, density estimation, numerical optimization, maximum likelihood estimation, classification, and resampling. To assess whether Statistics would be the best fit for you at UCLA, please go to this website: www.cac.ucla.edu/findyourmajor, To determine whether you may transfer a course from a public community college or university to UCLA, please go to this website: https://www.transferology.com/, Read the Transferable Courses section on this website for other details: http://www.registrar.ucla.edu/Student-Records/Transfer-Credit-Processing, The Department of Statistics at UCLA offers both a major and a minor in Statistics. Tutorial, three hours. Parameter estimation, hypothesis testing, and other statistical issues. Presentations and written reports required. Coverage of models used for forecasting only one measurement type and models used to forecast several types of measurements simultaneously. Exploration of standard methods in temporal and frequency analysis used in analysis of numerical time-series data. Letter grading. P/NP or letter grading. 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. Topics include Markov chain Monte Carlo computing, sequential Monte Carlo methods, belief propagation, partial differential equations. Requisite: course 100C (may be taken concurrently) or 101B. Lecture, two hours. Concurrently scheduled with course C183. Seminar, one hour. Not open for credit to students with credit for course 10, 11, or 13. Introduction to fundamentals of analysis of types of spatial and spatial-temporal datasets frequently arising in geostatistical problems. Letter grading. Variety of designs and methods, including experiments, matching, regression, panel methods, difference-in-differences, synthetic control methods, instrumental variable estimation, regression discontinuity designs, and sensitivity analysis. Preparation: basic statistics, linear algebra (matrix analysis), computer vision. Introduction to statistical methods developed and widely applied in several branches of computational biology, such as gene expression, sequence alignment, motif discovery, comparative genomics, and biological networks, with emphasis on understanding of basic statistical concepts and use of statistical inference to solve biological problems. P/NP or letter grading. (Same as Epidemiology M216.) Objectives and techniques of scientific writing and practice with different forms of professional writing. Lecture, three hours. The Department of Statistics at UCLA offers both a major and a minor in Statistics. (Formerly numbered 235.) Designed for graduate students. S/U or letter grading. Students solve real data science problems for community- or campus-based clients. Lecture, three hours; discussion, one hour. Letter grading. Letter grading. May be repeated for credit. Opportunity to solve real data analysis problems for real community-based or campus-based clients. Letter grading. Requisites: courses 10, 20, and 101A, or equivalent level of discipline. Lecture, three hours; discussion, one hour. Statistics and Methods; Data. Students work in small groups with faculty member and client to frame client's question in data science terms, create mathematical models, analyze data, and report results. Sufficiency, exponential families, least squares, maximum likelihood estimation, Bayesian estimation, Fisher information, Cramér/Rao inequality, Stein's estimate, empirical Bayes, shrinkage and penalty, confidence intervals. Study and research for MS thesis. Overview of fundamental concepts of data analysis and statistical inference and how these are applied in wide variety of settings. Best of luck to all applicants. To gain in-depth understanding of these methods, implementation of them in R, Python, and C++. Performance of simulations and analysis of real datasets using C, C++, and R. Fundamental principles and techniques for programming in these languages. Letter grading. Requisite: course 100C or 101A, and 100B. Introduction to Statistical Reasoning STATS XL 10 This introductory course covers statistical understanding including strengths and limitations of basic experimental designs, graphical and numerical summaries of data, inference, and regression as descriptive tool. R is currently state-of-art for statistical computing, simulation, statistical graphics, and analysis of data. Lecture, three hours. S/U or letter grading. Designed as adjunct to lower-division lecture course. Formulation of vision as Bayesian inference using models developed for designing artificial vision systems. Limited to Master of Applied Statistics students. Exploration of topics in greater depth through supplemental readings, papers, or other activities and led by lecture course instructor. Lecture, three hours. Survey sampling, estimation, testing, data summary, one- and two-sample problems. Computer vision and pattern recognition. HOME; SOFTWARE. Causal probability logic using directed acyclic graphs. Rejection sampling and importance sampling and their roles in MCMC. Applications drawn from various fields including political science, public policy, economics, and sociology. Preparation: apprentice personnel employment as teaching assistant, associate, or fellow. May be applied toward honors credit for eligible students. Limited to junior/senior statistics majors and minors. Prof. Song-Chun Zhu, sczhu@stat.ucla.edu, Office: Boelter Hall 9404 Office Hours: … Requisite: course 100B. Intended for Data Theory majors as introduction to Python language and libraries most frequently used in data science. Lecture, three hours; discussion, one hour. Topics include vector/matrix computation, multivariate normal distribution, principal component analysis, clustering analysis, gradient-based optimization, EM algorithm for missing data, and dynamic programming. Students are required to enroll in Statistics 290 each quarter, and are strongly encouraged to take Statistics 200A-200B-200C, 201A-201B-201C, and 202A-202B-202C in their first year. Limited to Master of Applied Statistics students. S/U or letter grading. Limited to Master of Applied Statistics students. May not be repeated. Introduction to broad range of algorithms for statistical inference and learning that could be used in vision, pattern recognition, speech, bioinformatics, data mining. Tools for data acquisition, transformation and analysis, data visualization, and machine learning and tools for reproducible data analysis, collaboration, and model deployment used by data scientists in practice. (Same as Environment M235.) Development of students' own research. Lower-Division Courses ; Upper-Division Courses ; Graduate Courses. Email: carloscinelli@ucla.edu yDepartment of Computer Science, Loyola Marymount University, Los Angeles. Request that the institution attended (this includes UCLA Extension) send us an official transcript. S/U grading. (Formerly numbered 200C.) Lecture, three hours. Designed as adjunct to lower-division lecture course. S/U grading. Enforced requisite: course 20. Distributions in several dimensions, partial and multiple correlation. S/U or letter grading. STATS 201A. Weekly meetings in classroom setting to study basic consulting skills, share experiences, exchange ideas, and make reports. Lecture, three hours; discussion, one hour. Community Engagement and Social Change Minor, Graduate Student Continuous Registration Policy, Nonresident Supplemental Tuition Exemptions, Health Sciences Summer Fees (Medicine, Dentistry), Undergraduate Study List Deadlines and Fees, Graduate Student Study List Deadlines and Fees, College of Letters and Science Diversity Requirement, Graduate School of Education and Information Studies Diversity Requirement, School of Public Affairs Diversity Requirement, School of the Arts and Architecture Diversity Requirement, Departments, Programs, and Freestanding Minors, Names, Changes, Special Marks, and Errors, Professional School and Extension Transcripts, Graduate Individual Studies Classes Master List, Course Inventory Management System (CIMS). S/U or letter grading. Enforced requisite: course 101B. Topics include statistical graphics, root finding, simulation, randomization testing, and bootstrapping. P/NP or letter grading. May be repeated for credit. Designed for graduate students (open to undergraduate students with consent of instructor). In Progress grading (credit to be given only on completion of course 141SL). Letter grading. Preparation: two terms of statistics or probability and statistics. Honors content noted on transcript. Requisites: course 32 or Program in Computing 10C with grade of C- or better, and one course from Civil Engineering 110, Electrical Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. Use of Python and other technologies for data analysis and data science. S/U or letter grading. Structural equation models, including path and simultaneous equation models. S/U or letter grading. How to handle data in different packages (input, output, data management, treatment of missing data), general syntax of different programming languages, and good practice for writing own statistical functions. Concurrently scheduled with course C245. (Same as Bioinformatics M222 and Chemistry CM260B.) Designed to provide understanding and perspectives on role of statistics in modern science, theory of statistics, and its strengths and weaknesses. Study of three types of spatial data: geostatistical data, lattice data, and point patterns, with emphasis on applications and analysis of spatial data using open-source statistical software R. P/NP or letter grading. Topics include temporal and frequency analysis, wavelets, and chaos. Seminar, two hours. S/U or letter grading. S/U grading. Implementation of code that executes inference and decision. Advancements in modern survey methodology. Exposure to realistic statistical and scientific problems that appear in typical interactions between statisticians and researchers, with lectures centered on case studies presented by faculty members and invited speakers from business and academic fields. Interaction with nonprofit organizations can be either on location or over the Internet. Requisites: courses 402, 403. R; Stata; SAS; SPSS; Mplus; Other Packages. Requisites: courses 100B or Mathematics 170S, 101A, 101C or Mathematics 156, Mathematics 118, 131A. Letter grading. Requisites: course 10 or 12 or 13 or Economics 41 or score of 4 or higher on Advanced Placement Statistics Examination, and course 20. Requisites: courses 200A, 231B. Metropolis and Gibbs sampling algorithms. Lecture, three hours. Limited to Master of Applied Statistics students. (Same as Biomathematics M280 and Biostatistics M280.) Extensions as simulated tempering. S/U or letter grading. (Same as Mathematics M148.) Lecture, three hours; discussion, one hour. Letter grading. Fundamental methods in longitudinal data analysis, with examples of actual applications in various disciplines. P/NP or letter grading. Lecture, three hours; discussion, one hour. Modern methods for constructing and evaluating statistical models, including non-Bayesian and Bayesian statistical modeling approaches. They are the minor class planning worksheet / minor contract and the form to add major / minor at 150+ units. Major concepts of social network theory and mathematical representation of social concepts such as role and position. (Same as Epidemiology M204.) Confirmatory factor analysis, covariance structure modeling, and multiple-group analysis. Honors content noted on transcript. Weekly meetings in classroom setting to study basic consulting skills, share experiences, exchange ideas, and make reports. Seminar, three hours. Lecture, three hours; discussion, one hour; laboratory, one hour. Lecture, three hours; discussion, one hour. Designed for physical and social sciences students who are interested in using statistics and its applications for forecasting and data-driven decisions and for life sciences and medical school students who are interested in modeling of historical data to predict outcomes. Letter grading. Practical tips regarding building, fitting, and understanding models provided.

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