Data Analytics Technology (DAT)

Business decision modeling is applied in all types of organizations to complement traditional approaches to managerial decision making. This course prepares managers to be active participants in the model building process by providing hands-on experience using realistic data and commercial-level Excel software ad-ins. The modeling approach is successfully applied to decision problems in human resource management, service delivery systems, marketing, finance, production and logistics. Applications of linear programming, non-linear programming, PERT/CPM, forecasting, decision trees, queuing and Monte Carlo simulation are covered in this course.

This is an application oriented course aimed at developing skills in getting, exploring, manipulating, analyzing, and presenting business data using data visualizations. It will employ visualization software such as Tableau.

This course will develop knowledge and skills in the use of predictive models for improving stakeholder outcomes. It will cover a wide array of supervised learning techniques from linear regression to support vector machines. Students will learn data wrangling approaches for enhancing the power of predictive models. In this hands-on course predictive models will be built using R.

This course is designed to equip students with data analysis techniques and skills tailored for business applications. The course will use R or Python, the two leading programming languages used by data scientists, to transform data into actionable insights, driving business strategy and innovation. Analysis techniques discussed will include techniques such as cluster analysis, dimension reduction, natural language processing, time series analysis, and network analysis.