![]() ![]() This is a course in statistical data mining II with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. Variable Selection for linear regression and generalized linear regression. This is a course in statistical data mining with emphasis on hands-on data analysis experience using various statistical methods and major statistical software (SAS and R) to analyze large complex real world data. The use of visual basic for applications for the development of applications of management science models for planning and decision support in a spreadsheet environment. It introduces students to design principles for creating meaningful displays of quantitative and qualitative data to facilitate managerial decision-making. This course provides an introduction as well as hands-on experience in data visualization. ![]() Students will apply and integrate the data warehousing and business intelligence knowledge learned in this course in leading software packages.ĭata analytics certificate elective courses Course No. This course will introduce students to the design, development and operation of data warehouses. Data warehouses are useful in generating valuable control and decision-support business intelligence for many organizations in adjusting to their competitive environment. Data warehouses are used to store (archive) data from operational information systems. This course is designed for the comprehensive learning of data warehousing technology for business intelligence. This course is intended for users of existing databases to extract needed information and should not be taken by MSIS students or those students who wish to learn detailed database design techniques.ĭata Warehousing for Business Intelligence Students who complete this course should understand how to use SQL for basic data manipulation and queries. The course introduces the structured query language (SQL) used to manage data. This course provides an introduction to the use and design of databases to store, manipulate and query data. Students are required to analyze data using major statistical software SAS and R. Various aspects of linear and logistic regression models are introduced, with emphasis on real data applications. This course covers the fundamental concepts of applied data analysis methods. Elementary analyses may include measures of location and spread, correlation, detection of outliers, table creation, graphical displays, comparison of groups, as well as specialized analyses. Data management and manipulation techniques including queries in SQL will be covered. The focus is on a few popular data management and statistical software packages such as SQL, SAS, SPSS, S Plus, R, and JMP although others may be considered. This is a course on the use of computer tools for data management and analysis.
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