After following this course, the students will be able to:
- discuss the advantages of modern analytics for businesses;
- understand the business analytics tools and techniques
- apply meta-heuristics for optimization of operational processes
- apply analytics for improved decision support (to problems in logistics, finance and marketing) and understand the ramifications of prescriptive analytics for delivering marketable products and solutions
This course focuses in particular on prescriptive analytics, which focuses on what will happen and when and why. Moreover, it suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. We will discuss optimization techniques to make better decisions supported by existing data.
The key course subjects include prescriptive analytics and optimization methods. We will work in teams on real-life case studies, like in logistics, economics, finance and marketing. The large case study at the end of this course will most likely be about dynamic pricing, like applied for flight, holiday and hotel bookings.
The students will work in teams on real-life case studies using real world dataset in logistics, finance and marketing. The large case study at the end of this course will most likely be about dynamic pricing, like applied for flight, holiday and hotel bookings.
The course is divided into four modules. Each module (appr. 4 weeks) consists of lectures and assignments (individual and/or in groups). Topics included in each module are:
Module 1: Introduction, predictive analytics versus prescriptive analytics, optimization methods (linear)
Module 2: Optimization methods (integer, robust), Meta-heuristics, applications in logistics, finance
Module 3: Applications in marketing, internet, health care, public organizations
Module 4: dynamic pricing and (group) project work, presentations
Type of instructions
Lectures, Lab sessions, Self-study, Group work (research, writing reports and giving presentations)
Type of exams
Assignments (60%) and written exam (40%)
- Bertsimas, D., O'Hair, A., Pulleyblank, W., The Analytics Edge. Required papers and chapters will be uploaded unto Blackboard
- Recommended literature will be uploaded unto Blackboard.
The courses from the Data Science and Entrepreneurship program require specific prior knowledge. It is only possible to participate in this course if approved by the admission committee and if you are enrolled for the program. Please note that this course will be taught in Mariënburg, ‘s-Hertogenbosch (JADS). For more information, contact the Program Coordinator (via firstname.lastname@example.org).