Data Analytics - SAS Certification
SAS Certification courses
Program: MBA
This course presents the principles and techniques of widely used statistical software systems, such as SAS, for data management (information storage and retrieval), data modification, file handling, and statistical analysis and reporting. The course covers special features such as graphics, macro languages, software and/or library interfacing and the basics of data mining. Classes are held in computer labs, and half of the time is devoted to lab work.
Prerequisites & notes
Prerequisite/Corequisite: The following courses must be completed previously: MBA 643.
Students who have received credit for the topic Statistical Software for Data Management and Analysis under a DESC 695 number may not take this course for credit.
Program: MBA
Reliable managerial forecasts of business variables must often be obtained against a background of structural changes in markets. This course focuses on the theory and applications of the most widely used methods of forecasting including decomposition methods, exponential smoothing, and the Box-Jenkins (ARIMA Building) techniques for non-seasonal and seasonal modelling. Recent approaches in forecasting such as artificial neural networks are also introduced. Business and economic databases are analyzed using statistical software packages in both class and project assignments. Component(s): Lecture
Prerequisites & notes
Prerequisite/Corequisite: The following courses must be completed previously: MBA 643.
Students who have received credit for DESC 677 may not take this course for credit.
Program: MBA
The course covers essential ideas and techniques for extracting information from large amounts of data. It discusses both supervised and unsupervised methods and covers topics such as dimension reduction, multiple regression, logistic regression, discriminant analysis, classification and regression trees, neural networks, association rules, cluster analysis and multi-dimensional scaling. Illustrations of the concepts and methods are given, and students gain practical experience in data mining with the use of popular data mining software. Component(s): Lecture
Prerequisites & notes
Prerequisite/Corequisite: The following courses must be completed previously: MBA 643.
Students who have received credit for the topic Data Mining Techniques under a DESC 695 number may not take BSTA 678 for credit.
Program: MBA
This course introduces and examines the role of contemporary statistical methods in improving business and industrial processes. The methodologies selected for discussion represent those that are most extensively used in contemporary business studies and analyses. The topics covered include modern statistical thinking, linear regression analysis, logistic regression, and experimental methods in product and process designs. The course involves mostly analyses of real-life data using statistical software packages. The understanding of the rationale of the methodologies introduced is also emphasized. Component(s): Lecture
Prerequisites & notes
Prerequisite/Corequisite: The following courses must be completed previously: MBA 643.
Students who have received credit for the topic Statistical Models for Data Analysis under BTM 695 may not take this course for credit.
Other Business Analytics elective(s)
Program: MBA
The subject matter for this course varies from term to term. Students may take this course more than once, provided that the course content has changed.
