Business analytics involves uncovering the hidden information in masses of business data using statistical models and algorithms. In this unit, some of the most widely used prediction and classification models will be covered. A suitable software environment for business analytics will be used, and tools for handling large data sets will be introduced.
We will explore the trade-off and distinction between prediction, explanation and interpretation using statistical models. Topics to be covered include numerical optimisation; Monte Carlo simulation; resampling methods such as the bootstrap, cross-validation, and bagging; nonlinear and nonparametric methods such as regression splines, trees and support vector machines; principal components analysis and clustering.
Introductory statistics for honors level undergraduate students. The objectives of the course are to help students develop an understanding of statistical thinking and to enable students to apply basic statistical techniques. By the end of the course students should be informed and critical consumers of quantitative arguments.
Stat/Engl 332 - Visual Communication of Quantitative Information
This course will help prepare students to be active citizens in the information technology age. Students will develop critical thinking skills about how information is visually presented, and they will learn how to accurately and attractively communicate quantitative information using graphics. At the end of the course students will: (1) know about important historical and contemporary examples, (2) know about and how to implement the elements of graphical design, (3) be able to evaluate visual presentations of information in the media, and, (4) be able to use the computer to generate graphics to communicate information effectively
(Server down) The objectives of the course are to help students: (1) Grasp the concepts and develop critical thinking in multivariate statistical analysis. (2) Learn about multivariate problems. (3) Compute analyses using standard statistical software. (4) Learn suffcient vocabulary to read further about new methodology. (5) Apply the methodology to new problems.
(Server down)Approaches to finding the unexpected in data: data mining, pattern recognition and understanding. Emphasis is on data-centered, non-inferential statistics, for large or high-dimensional data, and topical problems. Simple graphical methods, as well as classical and computer-intensive methods applied in an exploratory manner.
Introduction to statistical computing for data analysis. Reading and working with dfferent data formats: flat files, databases, web technologies, netCDF. Working with massive data, handling missing data. ffcient programming, reproducible code. Primarily using R but other software SAS, unix commands, awk, python as needed to conduct data analysis. Topical real data problems.