This course enables students to establish the difference between databases and data warehousing. Students will learn how to manage and create data warehouses from large datasets in a corporate setting. Since this is a new trend in how to implement data, there will also be exploration of possible improvements to data warehousing.

This course provides an introduction to the field of data visualization. Students learn basic visualization design principles to produce meaningful displays of quantitative and qualitative data in order to enhance the managerial decision making process. Students will learn various techniques for visualizing sequential, text-based, geospatial, hierarchical data and large data sets. Foci covered include data selection, data cleaning, data analysis, data presentation methods. Students will apply analysis and data visualization design principles to the design of interactive business dashboards and reports. Students will present their work in multiple formats to a range of audiences. Students will be introduced to various visualization software tools.
 

The focus of this course is mainly on prescriptive analytics with some parts focused on predictive analytics. Prescriptive analytics seeks to determine the best solution or outcome among various alternatives for a given situation, as well as suggest decision options for how to take advantage of a future opportunity or mitigate a future risk and illustrate the implications of each decision option. In this course, students will be familiar with the techniques, tools and applications that support managerial decision-making. The emphasis will be on the formulation of different optimization problems and the use of appropriate quantitative techniques to solve these problems.

This course offers an introduction to tools to enhance managerial decision making at all levels of the organization and across business units. Students will be presented with a question, problem or decision and will be asked to develop solutions using data techniques. The data will be extracted from an array of sources (internal or external, data format). Students will also be asked to choose the appropriate models, tools and methods for analysis.

The capstone course provides students with an opportunity to synthesize what they have learned about analytics during their graduate degree program. Students will integrate and apply analytical skills and knowledge acquired in the previous courses – including data management, big data, visualization, data mining, predictive and optimization techniques, and statistics – to complete a project involving actual data in a realistic setting.