Foundations of Analytics and Data Visualization

MET CS 544

Prereq: (MET CS 546 and (MET CS 520 or MET CS 521)) or equivalent knowledge or instructor's consent. Formerly titled Foundations of Analytics with R. Provides students with the mathematical and practical background required in the field of data analytics. Probability and statistics concepts are reviewed as well as the R tool for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using the R. Laboratory course. 4 cr. Tuition:

Introduction to Probability and Statistics

MET CS 546

Undergraduate Prerequisites: Academic background that includes the material covered in a standard course on college algebra. - Prereq: an academic background that includes the material covered in a standard course on college algebra or instructor's consent. Provides students with the mathematical fundamentals required for successful quantitative analysis of problems. The first part of the course introduces the mathematical prerequisites for understanding probability and statistics. Topics include combinatorial mathematics, functions, and the fundamentals of differentiation and integration. The second part of the course concentrates on the study of elementary probability theory and discrete and continuous distributions. Restrictions for undergraduate students: This course may not be taken in conjunction with MET MA 213; only one of these courses will count toward degree program requirements. Students who have taken MET MA 113 as well as MET MA 123 will also not be allowed to count MET CS 546 toward degree requirements.

SAS with Statistical Applications

MET MA 603

Undergraduate Prerequisites: MET or CAS MA 214 - Offers a unified and in-depth coverage of the statistical computer package SAS and its statistical applications. Topics include the language of SAS, data formatting, creating and storing SAS data sets, fi le manipulations, macros, and graphics. Also included are procedures for statistical techniques selected from analysis of variance, regression, factor analysis, scoring, and categorical data analysis. Several large data sets are used as case studies, emphasizing hands-on experience with SAS for Windows. Laboratory course. 4 cr. Tuition: $3900