Mercator Endowed Chair of Demand Management & Sustainable Transport

Predictive Analytics 

This course is dedicated to conveying a sense of how analytics projects work so as to be able to manage them and/or assess their merit. 

It is not a modelling course - although we will do modelling. It is also not a programming course - although we will do plenty of programming in R. Instead, the modelling and programming just serves as an illustration of the steps featured in typical analytics projects. This should help in the planning of such a project, starting from understanding of the business problem over modelling up to model assessment and communication of the project's results (or a project proposal) to a client.

There is no classic split between lecture sessions and tutorial sessions; instead, lecture elements, practical demonstrations and exercises are mixed together in all sessions so as to create a more engaging environment. In an assessed groupwork, you will go through all the stages of a data science project including shaping the business objectives and connecting the modelling results to them.

The syllabus looks as follows: Introduction to the CRISP-DM process (business understanding), Sampling and Partitioning (data preparation), Information selection, modelling and overfitting (modelling), Model evaluation, Applications: Naïve Bayes, association mining, clustering, text mining.

Predictive Analytics in Finance

This course is dedicated to conveying a sense of how to structure analytic projects systematically in the context of predictive models. The course introduces such a structure with an applied, step-by-step introduction to predictive analytics that mixes theory and practical, hands-on implementation tasks (using programming in R). Fundamental types of predictive data science models are introduced, including decision trees, logistic regression, support vector machines, neural networks, and naïve Bayes. In addition to those supervised models, we also look into unsupervised models for clustering.  The aim is to enable students to work more efficiently alongside data scientists, and to empower students to conduct predictive modelling themselves using common machine learning approaches.

The course features hands-on exercises and case studies in Finance, such as fraud detection, default/return prediction in a peer-to-peer investment case (based on real data from Lending Club) and customized credit product pricing (based on real data from a car loan provider).

Prescriptive Analytics & Machine Learning

This module seeks to familiarize students with the main tools used in the domain of prescriptive analytics (i.e., decision support via optimization techniques) and the use of machine learning methods within this domain. The module builds on the prerequisite module “Predictive Analytics” where basic machine learning methods were already introduced.

1. Introduction: overview of prescriptive analytics
2. Static optimization problems & applications:
   1. Linear programming
   2. Non-linear optimisation
   3. (Mixed) Integer optimisation
3. Dynamic optimization problems
   1. Dynamic programming
   2. Reinforcement learning / Q-learning

4. Interpretability in machine learning

    

Pricing Analytics

In this course, we discuss demand estimation (including discrete choice modelling) and demand forecasting (including using transformers) as the basis for pricing and revenue management, as well as capacity control (classical revenue management) and pricing control (dynamic pricing, markdown pricing, B2B pricing). We use plenty of case studies, generative AI and hands-on implementations in class to train application of state-of-the-art pricing techniques.

Business & Analytics Integrator Skills

In this module, we combine what has been learned over the course of the MSc Business Analytics program so far to now hone your skills as Analytics Managers. This module covers the whole journey from building and managing a data science team, over setting up data science projects, to managing them and deploying their outputs. It further focuses on translating analytics and AI algorithms into innovative solutions and new business opportunities and vice versa.

We make use of the current generative and other AI tools and algorithms to enhance your leadership skills to effectively design and implement analytics and AI projects in the organization.  More specifically, this includes: 

1. Building and managing a data analytics and AI team
2. Writing, pitching and assessing data  analytics and AI initiatives
3. Understanding and addressing ethical considerations in data analytics and AI projects
4.  Translating analytics and AI goals into business goals. Translating analytics and AI algorithms into innovative solutions and new business opportunities.
5. Overcoming barriers in the organization to implement novel analytics and AI solutions.
6. Managing and leading cross-functional analytics and AI innovation teams.
7. Understanding and implementing best practices, tools and methods to manage innovation and change, such as agile new product development (scrum etc.), stage-gate processes, metrics and project evaluation tools etc.

PT MBA / FT MBA / Online MBA - Data Science & AI

With the dramatically increased use of data science in business there comes an even higher increased need for managers with knowledge of the fundamentals of data science to make effective decisions: McKinsey estimated that about 10 managers with these skills will be needed for every data scientist (because leverage from a data science team can be gotten in multiple areas of the business).

This course seeks to impart this knowledge. Specifically, the objective is to convey an understanding of data science sufficient to become a critical consumer of data science solutions. You will acquire the skills needed to ask the right questions when consultants are proposing data science projects, and you will be able to communicate better with internal data science teams as you will have an understanding of how data scientists work. The aim is not to train you to become a data scientist, but to work with them as a manager.

The following concepts are covered (taught in a hands-on, case-based manner):

• Introduction to the Cross-Industry Standard Process for Data Mining: from business understanding over data understanding, data preparation, modelling, evaluation to deployment.
• Data types and why this matters
• Data sampling and partitioning
• Conceptual understanding of key machine learning models for predictive analytics (decision trees, linear classifiers, …)
• What is a good model? Evaluation and visualisation of model performance
• Data Science and business strategy: assessing data science project proposals, working with data scientists
• A manager's guide to Generative AI
• Visualization concepts, interactive maps and dashboards: theory and practice using Tableau

Fundamentals of Optimization – Doctoral Program

Optimization is important to many applications in business, be that finance, operations, marketing or others. This course aims to provide a broad overview of the concepts that underpin optimization to help students to gain an understanding of what type of optimization problem they may be dealing with in their studies, and how this could be tackled.

Coverage includes:

• Structure of an optimization problem
• Deterministic versus stochastic optimization
• Continuous versus discrete optimization
• Constrained versus unconstrained optimization
• Fundamentally important concepts like convexity, duality, complexity, total unimodularity, ...
• Introduction to various techniques including linear and non-linear mathematical programming, (approximate) dynamic programming for control problems, optimal learning

We will not go overly deep into the topics due to time constraints; instead, the focus is on imparting an intuitive understanding of optimization techniques and of structures that can be exploited. The intention is to make this course useful and relevant to any students who face some form of optimization problem and who do not yet have received formal training in optimization.