Search result: Catalogue data in Autumn Semester 2021
MAS in Management, Technology, and Economics  MAS MTEC Introductory Event for 1st Semester Students. Monday, 20.09.2021, 16.00 - 17.15 h, HG E 1.2 (tbc) | ||||||
 3. Semester | ||||||
| Core Courses | ||||||
| Quantitative and Qualitative Methods for Solving Complex Problems | ||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|---|
| 363-0305-00L | Empirical Methods in Management | W+ | 3 credits | 2G | S. Tillmanns | |
| Abstract | In this class, students learn how to understand and conduct empirical research. It will enable them to manage a business based on evident-based decision-making. The class includes group assignments, where students will cover small parts of the lecture content in self-created videos. | |||||
| Objective | The general objective of the course is to enable students to understand the basic principles of empirical studies. After successfully passing the class, they will be able to formulate research questions, design empirical studies, and analyze data by using basic statistical approaches. | |||||
| Content | Data has become an important resource in today’s business environment, which can be used to make better management decisions. However, evidence-based decision-making comes along with challenges and requires a basic understand of statistical approaches. Therefore, this class introduces problems and key concepts of empirical research, which might be qualitative or quantitative in its nature. Concerning qualitative research, students learn how to conduct and evaluate interviews. In the area of quantitative research, they learn how to apply measurement and scaling methods and conduct experiments. In addition, basic statistical analyses like a variance analysis and how to conduct it in a standard statistical software package like SPSS are also part of the lecture. The lessons learned from the lecture will empower students to critically assess the quality and outcomes of studies published in the media and scientific journals, which might form a basis auf their decision-making. We recommend the lecture also to students without basic statistical skill, who plan to attend more advanced lectures in the field of artificial intelligence such as Marketing Analytics. The lecture will be taught online this fall semester. Therefore, it involves group work, where students form groups in order to create small learning videos, which cover small parts of the lecture. These videos will be shown and discussed in the online lecture and will make up 30% of the final grade. Part of this assignment will be the evaluation of videos from other students. The preparation of the videos will also prepare students for the final exam. In addition to that, there will be some non-mandatory online exercises as an additional opportunity to prepare for the exam. | |||||
| Literature | Literature and readings will be announced. For a basic undertanding we recommend the Handbook of Good Research by Jürgen Brock and Florian von Wangenheim. | |||||
| Prerequisites / Notice | The course includes out-of-class assignments and projects to give students some hands-on experience in conducting empirical research in management. Projects will focus on one particular aspect of empirical research, like the formulation of a research question or the design of a study. Students will form groups and create a learning video regarding one specific topic. Assignments will be graded and need to be turned-in on time as they will be shown and discussed in class. Students will also have to evaluate the videos of other student groups. Online class participation is encouraged and can greatly improve students' learning. In this spirit, students are expected to attend class regularly and come to class prepared. | |||||
| 363-1004-00L | Operations Research | W+ | 3 credits | 2G | S. Bütikofer van Oordt | |
| Abstract | This course provides an introduction to operations research methods in the fields of management science and economics. Requisite mathematical concepts are introduced with a practical, problem-solving perspective. | |||||
| Objective | - Introduction to building and using quantitative models in a business / industrial environment - Introduction to basic optimization techniques (Linear Programming and extensions, network flows, integer programming, dynamic and stochastic optimization) - Understanding the integration of quantitative models into the managerial decision process | |||||
| Content | The economic environment of today's companies is characterized by high cost pressure, declining margins, intensified international competition, rising customer requirements and increasingly strict regulations. Strategic and operational decisions at all management levels are becoming more and more complex due to the increasing amount of data, interrelationships, conditions and target criteria to be considered. Often it is no longer possible to solve operational tasks with experience and common sense alone and to adequately estimate the consequences of decisions without software support. Quantitative models and methods of operations research and operations management offer decision support for complex problems. Mathematical optimization models are used to precisely formulate operational decision problems so that they can subsequently be analysed and optimized using suitable solution methods. A large number of quantitative real-world problems can be formulated and solved in this general framework. Applications of operations research comprise, for instance, decision problems in production planning, supply chain management, transportation networks, machine and workforce scheduling, blending of components, telecommunication network design, airline fleet assignment and revenue management. This course offers an introduction to operations research, emphasizing basic methodologies and underlying mathematical structures. The following topics are covered in detail: - Introduction to system modelling and operations research - Linear models and the importance of linear programming - Duality theory in linear programming and shadow prices - Integer programming - Dynamic optimization (under uncertainty) and applications in inventory management. | |||||
| Lecture notes | A printed script will be made available. | |||||
| Literature | Any standard textbook in Operations Research is a useful complement to the course. | |||||
| Prerequisites / Notice | Undergraduate calculus, linear algebra, probability and statistics are a prerequisite. | |||||
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