Master of Business Administration with a concentration in Applied Business Analytics online

This course will help students to develop a comprehensive analysis of the micro and macroeconomic forces that shape and modify our economy. Supply and Demand, Revenue and Cost, wages, employment, regulations and taxation are discussed as they affect production, consumption, investment, trade, prices, quantities, profits, inequality, and the economy as a whole.

This course includes the principles of programming and software development using Python programming language. This class emphasizes the applications of computer programming based on real-life examples in various fields of business including Business Analytics, Management, Marketing, Finance, among others. Upon completion of the course, students will be able to understand computational concepts in addition to the fundamentals of the Python programming language. In addition, this course will cover the manipulation and use of data to solve businesses problems. In this course, students will be introduced to software packages for data manipulation and development of solutions to statistical and mathematical problems. In addition, we will apply machine learning and statistical methods to data obtained from online sources.

This course covers two related topics in the age of big data and knowledge discovery: data warehousing and data mining — their concepts, principles, and techniques. Topics in data warehousing include data warehouse/data mart architecture; multi-dimensional model design; extracting, transforming, and loading strategies. Topics in data mining range from statistics to machine learning; including techniques of clustering, association rules, and classification. OLAP (On-Line Analytical Processing) applications and business intelligence are also introduced.

This course is designed to provide a broad overview of machine learning concepts and applications aimed at automating and advancing analytics performance. The course distinguishes itself by anchoring its content on big data phenomena and thus domain knowledge, technology, and math are integrated throughout the course. The course covers five broad segments of topics in machine learning driven analytics: Fundamentals (Algebra, Matrices, and Probability for ML), algorithms (Optimization Techniques), supervised learning, unsupervised learning, and managerial application (Thought Leadership and usage of ML in big-data analytics). This course provides a unique balance between theory and application along with data driven cases — thus it aims to impart expert skills along with encouraging thought leadership. An integral part of machine learning analytics is the use of IT tools to support the organization and analysis of data — hence the students will learn to apply various machine learning analytics tools including R.