Please find here an overview of all modules that you can take in the Business Analytics & Operations Research program. When selecting and designing our courses, we always try to ensure that our courses systematically build on each other and do not overlap and. In this way, we ensure that our students establish successive competencies in the various methods and subject areas during the course of their studies.
Further information about our courses
Here you will find some examples of how you could build up your studies to prepare for a certain career path.
Example Track: OR Analyst
In the first semester the basics of Operations Research and Decision Theory are taught. OPL enables you to implement your own models and solve them using the software. In the introductory course Simulation you will learn to carry out simulation studies independently.
The module Algorithms and Data Structures extends existing knowledge about programming from the first semester. In the course Programming Heuristics you can apply this knowledge. Stochastic Models and Plant Simulation in combination with the course Simulation from the first semester prepare you for a possible career as a simulation expert.
In the third semester your knowledge of methods is now at a very high level. It is time to put your skills to the test in a specialist area. Courses from the Supply Chain Management area are listed here as examples. In the seminar on Supply Chain Management and in the seminar on Operations Research you will write a scientific paper on a current research topic, supported by our employees.
- Master Thesis
In the fourth and last semester you will write your master thesis. For example, it could be a good idea to continue your scientific work from the third semester.
Of course, there is also the possibility to write your master thesis in cooperation with a company. OR analysts are very welcome in companies – also and especially during their master thesis.
Example Track: Data Scientist
- Service Analytics
- Stochastic Models
- Digital Business Models and Technologies
- Computational Statistics with R
- Theoretical foundations of classical statistics and econometrics
In the third semester your knowledge of forecasting methods will be deepened and you will learn methods of the field of data science. Algorithms and Data Structures will also expand your existing knowledge of programming from the first two semesters. In the project module Business Informatics you will write a scientific paper on a current topic of business informatics and thus learn important knowledge that will be helpful for your master thesis.
- Master Thesis
In the fourth and last semester you will write your master thesis. For example, it could be a good idea to continue your scientific work from the third semester.
Of course, there is also the possibility to write your master thesis in cooperation with a company. Data Scientists are very welcome in companies – also and especially during the master thesis.
Operations Research Modules
Here you can find all currently offered courses in the Track Operations Research.
The course provides the conceptual basis of Operations Research and gives an introduction to mathematical modeling (e.g. graph theory, linear models) of real business problems. Based on this, different optimization methods are presented (e.g. integer and combinatorial optimization or dynamic optimization). The aim of the course is to enable students to model and solve problems independently.
The module teaches distribution theoretical basics and concepts of stochastic processes. Building on this, the modeling of stochastic processes (e.g. by Markow chains or queue models) is taught. The course enables students to model and analyze stochastic systems and processes independently.
In comparison to simulation, the models are solved analytically.
Since many years simulation has been one of the most important methods for studying dynamic processes. Many investments in production and logistics are only made after appropriate validation through the use of simulations. In this course, our students learn the necessary methodological knowledge for the implementation of simulation studies and the practical application of the simulation software (AnyLogic).
The course provides an in-depth understanding of the current methods and concepts of process planning in material goods and service companies. Based on mathematical models describing a business process, students deepen their competencies for the application of optimal solution methods as well as heuristics for more complex models. The aim of the course is to provide technical competence so that students are able to analyze and model complex problems independently and to solve them with a suitable method.
Simulation with Plant Simulation
The module is an advanced course in the field of discrete event simulation and is aimed at students who are already familiar with the basics of simulation. This is followed by an introduction to the simulation tool Plant Simulation. The course combines simulation with object-oriented programming to map properties of complex systems. At the end of this course, students will be able to independently create simulation models with individual control algorithms.
Seminar on Operations Research
Object-oriented programming of heuristics
Modeling and Optimization with OPL
In this course, students learn to represent real problems by mathematical models and to solve them in CPLEX Optimization Studio on the PC. Advanced techniques of linear modeling and the use of the modeling language OPL are taught.
Business Analytics Modules
Here you can find all currently offered courses in the Track Business Analytics.
With the increasing importance of services and digitization, data-based insights into customer preferences and benefits are becoming increasingly central to anticipating customer needs and behaviors and recommending or offering the right products or services. The course Service Analytics deals with data analysis methods for tasks in digital commerce and customer interaction. In particular, the algorithms of modern recommender systems are discussed and evaluated. In the exercise, students learn the language R and apply their knowledge to practical problems, develop and test recommender systems on realistic and large customer data sets.
Algorithms and Data Structures
Statistical Forecasting Methods
Students get an overview of the most important forecasting methods and models. They will also learn how to apply basic forecasting techniques to practical cases using the R software environment. The aim of the course is to enable students to understand and apply common forecasting models in a context-dependent manner and to interpret and critically question forecasts.
Time Series Analysis
In this course, students learn how to practically solve a case using statistical and/or data analysis methods of statistics. The focus is on methodological questions. In this course, students will write a seminar paper in order to demonstrate their competences in scientific work and in their research techniques.
In this course, students learn how to solve a practical case using econometric methods. The focus is on methodological questions. In this course, students will write a seminar paper in order to demonstrate their competences in scientific work and in their research techniques.
Quasi-experimental Policy Evaluation
In this module, students learn detailed methods of economic and econometric policy evaluation. Students will apply quantitative techniques to evaluate current policy measures in typical fields of economics.
Data Science with R
In this course, students acquire the conceptual and IT-technical basics for the preparation and analysis of data sets with the help of the interactive statistical software environment R. Based on example data sets, the students will solve economic problems with the help of adequate tools and methods of data science. In addition, the basics of SQL databases, MySQL and SQLite are taught.
Theoretical Foundations of Classical Statistics and Econometrics
Reading course on statistics
In this module, book chapters or journal articles on a selected statistical topic are discussed and critically questioned. In this way, students develop cognitive abilities to acquire new knowledge and new methods in the field of econometrics on their own.
Reading course on Econometrics
In this module, book chapters or journal articles on a selected topic of econometrics are discussed and critically questioned. In this way, students develop cognitive abilities to acquire new knowledge and new methods in the field of econometrics on their own.
Supply Chain Management Modules
Here you can find all currently offered courses in the field of Supply Chain Management.
Configuration of Production and Logistics Systems
Students gain an in-depth understanding of the current methods and concepts for configuring production and logistics systems. They get to know the relevant quantitative decision models and some practice-relevant solution algorithms and acquire the ability to put the acquired knowledge into practice during the development of planning systems.
Semninar on Supply Chain Management
Logistics: Inventory and Transport Management
Supply Chain Business Game
The course uses the business simulation game “The Fresh Connection”. In this business game, students take up different roles in a supply chain, including purchasing, production, logistics, sales and supply chain management, and make strategic, tactical and operational supply chain decisions. In addition, the students learn the current methods and concepts of planning and control in the supply chain and experience the effects of decisions on the business success of a company.
Supply Chain Planning
This course focuses on quantitative methods of service operations and revenue management as well as on their application in the service industry, e.g. with airlines, car rental companies, tour operators and accommodation providers. In service operations, the main focus is on aligning demand and capacity and on optimizing service processes. Within the framework of revenue management, the concepts of price differentiation, capacity control, overbooking control, and dynamic pricing are dealt with.
Modeling and Analysis of Retail Operations
The course provides an overview of current methods and concepts in the field of retail operations. Decision models and analytical methods are discussed, which are used in retail practice for problem analysis and decision support. In this context, the current developments in online retail, multi-channel retail and/or omnichannel retail will also be discussed.
Analytical Customer Management
This course deals with the management of acquisition and new customers as well as with the management of long-term customer loyalty of profitable customers from a company’s point of view. All central topics of customer management are considered regarding the background of maximizing the value of the customer base (“Customer Equity”). Consequently, customer acquisition, customer retention, and value-based customer migration are at the core of customer management. The aim of this course is, therefore, to convey the methodical concepts of customer management and their application to a large number of market-relevant problems by learning the necessary skills.
Customer Base Anaylsis
By participating in the course, students acquire a “toolbox” of stochastic models for customer evaluation, which are increasingly used in companies with comprehensive customer databases, such as mail-order companies. In addition, we teach mathematical basics as well as marketing-theoretical basics of transaction behavior, which enable students to create their own stochastic models. Students acquire the theoretical foundations of quantitative models in marketing and the ability to critically reflect on quantitative models and apply them to practical issues such as customer evaluation. Thus, students learn not only the theoretical basis of stochastic models but also their application to real data in order to perform customer evaluations.
Empirical Management Research
In the course “Empirical Management Research”, students acquire application-oriented specialist knowledge about relevant survey methods (e.g. questionnaire design) and analysis methods (e.g. regression analysis) in the field of social sciences. They understand how managers can apply these methods to business decision situations. Upon completion of the course, students will have the ability to determine the appropriate procedure for a practical problem, to perform it correctly, and to interpret the results logically to solve the problem. The accompanying exercise should make a significant contribution to the development of this methodological competence. The participants develop the ability to transfer the knowledge gained in the lecture to concrete questions. The students are able to apply the analysis methods accordingly and to evaluate them using the SPSS statistics software. After completing the course, students will have methodological competence in the field of social sciences, both on a theoretical and on an application-oriented level.
Digitalisation and Information Systems Modules
Digital Business Models and Technologies
The course conveys theoretical and practical concepts of how today’s information systems and technologies, together with advanced algorithms and data analysis, enable new, disruptive digital business models and systems. In case studies and the development and analysis of software components of digital business models, students will develop digital business models, evaluate them critically and from various perspectives, assess the requirements of information systems, and plan and implement digital transformations in a reflective manner. Students develop their own digital business models in teams and present them at different stages during the course.
Customer Relationship Management
Students of this module receive competences regarding the independent planning and processing of comprehensive tasks within the scope of CRM. The subareas of operative and analytical CRM are dealt with in detail as well as the course of the entire CRM process.
Here you will find all the courses currently offered in the field of finance.
Seminar Finance and Banking
In this module, students prepare a seminar paper on a current research topic and present it afterwards. By working on a current research topic, students gain deeper insights into current practical and theoretical questions of financing and banking management. Through the exchange with their fellow students and by defending their work, students also learn to present their own findings, to critically appreciate them and to defend them argumentatively. One of the aims of the course is to prepare students for the preparation of their master thesis.
Research Project Finance and Banking
In this module, students conduct empirical research at a high scientific level. From the respective formulation of the research question to the elaboration of an empirical model and its programming in statistical software to the interpretation of the results, all essential steps of empirical scientific work are carried out independently. Therefore, an advanced level of knowledge regarding the application of econometric methods is required. One of the aims of the course is to prepare students for the preparation of a later master thesis.
Credit Risk Modeling
In this course, students will learn various methods for modeling credit risks (e.g. option price theory, asset value model, risk-neutral default probabilities) at the individual and portfolio level. After completing the course, students will be able to link these methods to real problems and apply them independently with the help of Excel VBA. Students also learn to assess and classify the results of a risk analysis.
The module deals with the recording and management of various risk types (e.g. foreign currency risks, interest rate risks, credit default risks) that occur in financial companies. In addition to risk measures and their application to operational decision-making problems, supervisory regulations on risk limitation are also dealt with. The aim of the course is to provide students with knowledge in the measurement and control of risks in financial companies.
Here you will find all the courses currently offered on the ethics module.
Advanced Economic Ethics
This course provides students with in-depth knowledge of the theories and methods of welfare economics and distributive justice. The aim of this module is to enable students to perceive and analyze problems of the world economy and to develop solutions.
Social Innovation (SI)
Students of this module receive an overview of interdisciplinary concepts of innovation and social innovation, social entrepreneurship, and corporate citizenship. To this end, students develop an understanding of the social prerequisites for entrepreneurial action, with particular emphasis on entrepreneurial innovation.
Advanced Business Ethics
In this module, students are taught basic concepts of business ethics and are critically questioned. Students learn concepts such as corporate social responsibility, corporate citizenship, and sustainability. The aim of the course is for students to develop an understanding of the overarching interrelationships between different dimensions of responsible corporate actions in the context of social developments.
Business Ethics and Statistics
In this course, current social issues such as poverty and inequality are analyzed and questioned with the help of the R software environment. The aim of the course is to enable students to understand, question and determine key figures of business ethics.