This course lays the “Business” foundations for creating awareness in Business Analytics. Business analytics as a support for decision-making and its importance in the business environment is increasing at unprecedented levels. These enable executives, managers, and other corporate end-users to analyze various data and present actionable information to help make informed business decisions. We will appraise business data and analytics topics to address dynamic changes within an organizational context. 

This course aims to provide an overview of the research design, approaches, and methodologies that prepare students to conduct research activities. It will equip students with quantitative and qualitative modelling techniques to develop solutions that address contemporary business analytics challenges. Students will identify and formulate a real-world business research problem using the research principles in business analytics. Successful completion of the course will enable students to conduct research and perform analytics in a business environment.

This course introduces the basic concepts of applied statistics, including descriptive statistics, probability, and inferential statistics. It also covers linear regression, random variables, discrete, continuous random variables, basic and advanced calculus tools. The lectures will be threaded with tutorials that allow students to practice problem-solving in a business context. The course is the cornerstone of the coming courses in the Business Analytics Program as it lays the theoretical foundations and skills required to pursue the rest of the courses. 

This course introduces modeling, optimization, and simulation as applied to the study and analysis of operations to support effective and informed managerial decision-making. Optimization and decision systems provide a framework to think about a wide range of challenges and issues in business operations. The topics to be covered include a subset of the following: linear programming, sensitivity analysis for linear programs, duality, introduction to integer and non-linear programming, graph theory, convex optimization, and optimization algorithms. Examples are drawn from operations processes and systems. 

This course introduces the fundamental methods at the core of modern machine learning. It covers theoretical foundations and essential algorithms for unsupervised and supervised learning. In addition, it includes the foundations of reinforcement learning and deep learning. Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.