Mercator Endowed Chair

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.

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
• Visualization concepts, interactive maps and dashboards: theory and practice using Tableau

The course uses R to illustrate a data science project, but acquiring programming skills is no learning objective and, therefore, learning about R programming is entirely voluntary. 

The value proposition of making use of data to end up with better decisions is clear: “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” (Jim Barksdale, former president, and CEO of Netscape)

WHU's “Data Science for Decision Makers” program equips participants with the knowledge they need to successfully work with data scientists. The aim is not to train participants to become data scientists themselves, but rather to develop a level of data science understanding that reduces the communication barriers between decision makers and data scientists. You will learn how to make sense of data using machine learning tools and how to critically evaluate the merit of data science project proposals.

You will receive a WHU Executive Education Certificate after successfully completing the program.

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