Master in Management: Business Analytics Concentration
Master in Management / 12 June, 2018
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Professor for Supply Chain Management
Prof. Dr. Mirko Kremer is professor for Supply Chain Management at the Frankfurt School of Finance & Management since July 2014. His research focuses on the impact of managerial and customer (mis)behavior on the performance and design of Operations and Supply Chain systems, with a particular emphasis on micro-behavioral foundations of inventory management, sales forecasting, and queuing/service systems.

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Companies (and consumers alike) are drowning in data. We have smart home automation of everything including heating, lighting, fridges, toilets and multi-media. Industrial robots. Social media. Virtual showrooms. 3D glasses. Eye-tracking for assistive communication. Quickly evolving technologies and consumer habits provide companies with data in higher volume, higher variety and higher velocity than ever before.  With companies increasingly competing on data-driven business models, it is obviously convenient to have lots of data, but the real question is how to extract value from it to support decision making in strategy, marketing, finance, operations, and other areas of business.

In order to unlock the value inherent in data, companies need managers who combine business domain knowledge with solid methodological skills. This is the essential premise of the Master in Management Business Analytics concentration, which equips students with a state-of-the-art toolkit to solve business problems with real-world data. The concentration consists of four modules:

  • Digital Analytics
  • Marketing Information Analytics
  • Predictive Analytics
  • Prescriptive Analytics

Digital Analytics covers methodologies and algorithms to tackle the challenges companies face with large amounts of high-dimensional data. With classical statistical methods exhibiting some weaknesses in this area, new types of methods and algorithms have emerged at the intersection of statistics, machine learning, data visualization, and computer science. Benefitting from calculation power of modern computer technologies, these methods are today widely used in science and practice. The module provides you with essential computational skills and algorithms for finding and analyzing patterns in large-scale datasets. Topics to be covered in this module include data collection, integration, analysis, visualization, segmentation, classification, prediction and decision-making. Students will implement and apply the methods using software such as Excel and R.

Marketing Information Analytics is a methodological complement to the “digital analytics” module, with a strong focus on demand-side analytics and the implications for marketing strategy. With firms relying increasingly on vast amounts of data to inform their marketing decisions, you will learn the number-crunching skills needed to rigorously measure and assess the effectiveness of marketing strategies and tactics. Primary techniques that will be covered in lectures, case studies, and hands-on applications include regression analyses, cluster analysis, and forecasting.

Predictive Analytics introduces simulation as a powerful (yet intuitive) method for modeling complex systems, analyzing their behavior, and predicting the effects of managerial strategies. You will learn how to develop models, analyze and provide data to adequately parameterize and validate their models, conduct sensitivity analyses, and interpret and communicate results, acquiring the knowledge and tools to conduct simulation-based projects.

Prescriptive Analytics addresses optimal decision-making in settings with and without uncertainty. The module provides an introduction to the most relevant optimization techniques and their application in a wide range of business problems. The module places strong emphasis on model formulation and the use of appropriate software for solving real-world problems in industry and consulting.

The four modules span problems from all functional areas of business, and focus sharply on the practical side of business analytics. Each module provides the required technical background, and also offers students plenty hands-on learning opportunities through case studies and technical exercises. Software used in class includes both standard office applications and various specialized packages.

Furthermore, students can apply and further deepen their skill set by choosing from various elective modules, such as “blockchain”, “business process engineering”, “human and machine predictions”, or “artificial intelligence for managers”.

The Business Analytics concentration is suited for students interested in careers broadly defined around the term “data science”, and a useful complement to students that want to work in any functional area of companies that compete on data analytics and evidence-based decision-making. If you like data, are interested in advanced methods to extract business value from it, and are intrigued by business questions, this concentration is for you.