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1 - 10 of 30 results for: STATS ; Currently searching winter courses. You can expand your search to include all quarters

STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

STATS 112: Principles of Data Science (DATASCI 112)

A hands-on introduction to the principles and methods of data science. This course is designed to equip you with tools to begin extracting insights and making decisions from data in the real world, as well as to prepare you for further study in statistics, machine learning, and artificial intelligence. We will analyze and visualize data of different shapes and sizes (e.g., tabular, textual, hierarchical, geospatial). We will discuss common patterns and pitfalls of data analysis. We will build and evaluate machine learning models, focusing on general concepts (rather than specific methods), including supervised vs. unsupervised learning, training vs. testing error, hyperparameter tuning, and ensemble methods. The focus will be on intuition and implementation, rather than theory and math. Implementation will be in Python and Jupyter notebooks, using libraries such as pandas and scikit-learn. This course culminates in a project where you apply the ideas to a data science problem of your choosing. Website: http://dlsun.github.io/stats112 Prerequisite: CS 106a (or equivalent programming experience in Python). Note: All students must enroll in a discussion section that meets on Tuesdays and Thursdays in addition to the main lecture.
Terms: Win | Units: 5 | UG Reqs: WAY-AQR

STATS 160: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum | Units: 5

STATS 191: Introduction to Applied Statistics

Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Prerequisite: introductory statistical methods course. Recommended: 60, 110, or 141.
Terms: Win | Units: 3 | UG Reqs: GER:DB-Math, WAY-AQR

STATS 195: Introduction to R

This short course runs for weeks one through four of the quarter. It is recommended for students who want to use R in statistics, science or engineering courses, and for students who want to learn the basics of data science with R. The goal of the short course is to familiarize students with some of the most important R tools for data analysis. Lectures will focus on learning by example and assignments will be application-driven. No prior programming experience is assumed.
Terms: Win | Units: 1
Instructors: Jeong, Y. (PI)

STATS 199: Independent Study

For undergraduates.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit

STATS 200: Introduction to Statistical Inference

Terms: Aut, Win | Units: 4

STATS 203: Introduction to Regression Models and Analysis of Variance

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Prerequisites: A post-calculus introductory probability course, e.g. STATS 116, basic computer programming knowledge, some familiarity with matrix algebra, and a pre- or co-requisite post-calculus mathematical statistics course, e.g. STATS 200.
Terms: Win | Units: 3

STATS 208: Bootstrap, Cross-Validation, and Sample Re-use

By re-using the sample data, sometimes in ingenious ways, we can evaluate the accuracy of predictions, test the significance of a conclusion, place confidence bounds on an unknown parameter, select the best prediction architecture, and develop more accurate predictors. In this course, we will describe the many ways that samples get reused to achieve these goals, including the bootstrap, the parametric bootstrap, cross-validation, conformal prediction, random forests, and sample splitting. We also develop basic theory justifying such methods. Prerequisite: course in statistics or probability.
Terms: Win | Units: 3
Instructors: Sood, A. (PI)

STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (CHPR 206, EPI 206, MED 206)

Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.
Terms: Win | Units: 3
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