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1 - 10 of 72 results for: STATS ; Currently searching offered courses. You can also include unoffered courses

STATS 32: Introduction to R for Undergraduates

This short course runs for weeks one through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for data analysis. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, data transformation and visualization, simple statistical tests, etc, and some useful packages in R. Prerequisite: undergraduate student. Priority given to non-engineering students. Laptops necessary for use in class.
Terms: Aut, Spr | Units: 1

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 100: Mathematics of Sports

This course will teach you how statistics and probability can be applied in sports, in order to evaluate team and individual performance, build optimal in-game strategies and ensure fairness between participants. Topics will include examples drawn from multiple sports such as basketball, baseball, soccer, football and tennis. The course is intended to focus on data-based applications, and will involve computations in R with real data sets via tutorial sessions and homework assignments. Prereqs: No statistical or programming background is assumed, but introductory courses, e.g, Stats 60,101 or 116, are recommended. A prior knowledge of Linear Algebra (e.g., Math 51) and basic probability is strongly recommended.
Terms: Spr | Units: 3 | UG Reqs: GER:DB-Math, WAY-AQR
Instructors: Hartog, W. (PI)

STATS 101: Data Science 101

This course will provide a hands-on introduction to statistics and data science. Students will engage with fundamental ideas in inferential and computational thinking. Each week consists of three lectures and two labs, in which students will manipulate real-world data and learn about statistical and computational tools. Topics covered include introductions to data visualization techniques, summary statistics, regression, prediction, sampling variability, statistical testing, inference, and replicability. The objectives of this course are to have students (1) be able to connect data to underlying phenomena and think critically about conclusions drawn from data analysis, and (2) be knowledgeable about how to carry out their own data analysis later. Some statistical background or programming experience is helpful, but not required. The class will start with a brief introduction to R but will move at a relatively fast pace. Freshmen and sophomores interested in data science, computing, and statistics are encouraged to attend. Also open to graduate students.
Terms: Spr | Units: 5 | UG Reqs: GER: DB-NatSci, WAY-AQR
Instructors: Gablenz, P. (PI)

STATS 110: Statistical Methods in Engineering and the Physical Sciences

Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus. Please note that students must enroll in one section in addition to the main lecture.
Terms: Aut | 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 116: Theory of Probability

Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites: MATH 52 and familiarity with infinite series, or equivalent. Undergraduate students enroll for 5 units, graduate students enroll for 4 units. Undergraduate students must enroll in one section in addition to the main lecture. Sections are optional for graduate students.
Terms: Aut, Spr, Sum | Units: 5 | UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

STATS 141: Biostatistics (BIO 141)

Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Terms: Aut | Units: 5 | UG Reqs: GER:DB-Math, 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
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