Bachelor in Computational Business Analytics (BSc)

The course introduces the concepts, tools and principles of management in today’s global business context. Our journey begins with an introduction of what management is and a brief overview of management history. We then explore the external and internal environments that provide a context for how managers perform their jobs. We next examine how managers make decisions, as the decision making process is central to how organisations operate. The rest of the course is organised around the four functions of management, namely; planning, organising, leading, and controlling.

Lecturers:

Dr. Stevo Pavicevic

Dr. Jiping Li

On successful completion of this module, students will have a thorough comprehension of the basic and key concepts of bookkeeping, and a good understanding of accounting rules guiding financial statements preparation, i.e., they can:

- Describe the objectives of financial reporting
- Explain the flexibility inherent in accounting rules
- Outline the concepts and techniques of financial statement preparation

Lecturers:

Prof. Dr. Yuping Jia

This module pursues two objectives. Firstly, it trains students’ analytical skills and ability for logically valid and sound reasoning. Secondly, it assesses the philosophical foundations and normative implications of business studies and economics. As such, this module is all about asking probing questions. Finding answers to these questions should, in turn, make us think better and, ideally, act better.

This course will equip students with a basic understanding of essential concepts in the mathematical fields of calculus, algebra, numerical analysis, and optimisation theory that are required for many managerial as well as data science and machine learning applications. It serves as the building block for all other courses in the program.

Lecturers:

Dr. Pia Domschke

This course provides an introduction to data analysis using Microsoft Excel and Python/Pandas. The students receive a thorough introduction to programming with Python and its data science libraries; moreover, the students will also learn about the different programming paradigms underlying today's data science applications.

After successful completion of the module, students will be able to:

- Show the importance of finances for companies
- Analyse the investment and financing possibilities of companies
- Present the pricing of securities on the financial markets
- Explain portfolio and capital market theory and its practical relevance
- Examine the interaction of supply and demand on financial markets under different information scenarios

Lecturers:

Prof. Dr. Martin Faust

On successful completion of this module, students will be familiar with the basic concepts of decision theory and microeconomics, i. e. they can:

• Answer the basic economic question on how the interplay between individual decisions drives market outcomes and social reality.
• Outline the interrelation of decision theory and microeconomics
• Describe how decision processes are structured
• Emphasise the domain dependant relevance of different motives and preferences

Lecturers:

Prof. Dr. Heiko Karle

On successful completion of this module, students will have a thorough comprehension of stochastic concepts needed to treat statistical problems related to economic and financial issues, i.e. they can describe fundamental concepts from different fields of statistics and probability theory.

Lecturers:

Dr. Pia Domschke

This module will expose students to different types of algorithms that are used to numerically solve problems frequently occurring in managerial as well as data analysis applications. Students will also acquire a deeper understanding of the structure of the underlying optimisation problems, and thus be able to build computationally efficient models for data analysis.

This course will familiarise students with the basic concepts of databases, ranging from the creation of databases over data manipulation techniques to the extraction of relevant data. Students will also explore the challenges of working with different types of data.

This course is about how companies can design their operations to better match supply with demand and thereby gain a significant competitive advantage over their rivals. A better match of supply and demand can be achieved by using an adequate set of operations management tools, more specifically, by implementing rigorous quantitative models and well-understood qualitative operational strategies. This course introduces a broad range of operations management tools and aims at teaching the participants how and when to apply them in practical settings.

Lecturers:

Prof. Dr. Jürgen Strohhecker

On successful completion of this module, you can take responsibility for designing and implementing managerial accounting concepts in organisations. You will be able to critically analyse the suitability of different managerial accounting approaches for the specific context of their organisation. Moreover, you will learn to take into account the interdependencies of accounting choices when implementing managerial accounting concepts.

Lecturers:

Prof. Dr. Timo Vogelsang

This module investigates ethical and legal questions that the emergence of ever-larger datasets and advancements in (information) technology creates for management and society. Special focus will be given to issues related to privacy concerns and managerial responsibility, as well as considerations of fairness.

The first part of this course will expose students to different methods for data collection and familiarise them with the strengths and biases of the different methods. Students will also learn how to assess the trustworthiness of data. The second part of the course will give students an introduction to game theory and the role of incentives for managerial decision-making.

In this course, students learn about state-of-the-art data analytics and machine learning methods, with an emphasis on supervised learning. The course will also expose students to the fundamentals of statistical decision theory and its importance in managerial decision-making.

On successful completion of this module, students will have a thorough understanding of the most recent advances in corporate finance. Examples can include M&A, private equity, venture capital, IPOs, debt financing, trends in financial technology, and risk management. Students will gain an in-depth understanding of the most recent trends in the industry and will be able to apply their knowledge in practical settings.

Lecturers:

Prof Dr. Yigitcan Karabulut

Prof. Dr. Larissa Schäfer

The module provides basic knowledge and methods for analysing the interdependence of goods, labour, money, and capital markets in an economy. At the same time, it provides an insight into the major controversies between the most important economic schools and the consequences of these competing doctrines for the stabilisation policies of the government and the central bank.

Lecturers:

Prof. Dr. Benjamin Born

On successful completion of this module, students will have a thorough comprehension of Marketing, i.e. they can:

• Understand the terminology, concepts, and tools of modern marketing practice
• Comprehend the consumer decision-making process and the factors that affect it
• Explain the marketing mix (product management, price management, sales management, and communications management) and the importance of integrating these elements

Lecturers:

Dr. Britta Meinert

Dr. Tetyana Kosyakova

This module builds on the Computational Statistics course and covers fundamental machine learning techniques. Topics will cover a range of supervised and unsupervised learning algorithms. Students will understand how to employ the algorithms to solve a variety of practical problems ranging from financial time series forecasting to image processing.

This module bridges data science techniques with managerial decision-making. Students will learn how to effectively communicate the results of data analysis to decision-makers and how to derive actionable recommendations for management. They will also learn how to derive meaningful insights even in the presence of severely biased data.