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Business Informatics and Mathematics in Business and Economics (English)

Information on your course choice

Please note that you have to take the majority of classes at the School of Business Informatics and Mathematics. In most cases you do not need to register for courses, just attend the first lecture. In case you want to take courses outside from our school you can choose from the university wide electives list.

All undergraduate students are allowed to take graduate’s level courses at the School of Business Informatics and Mathematics and the other way around. For the most part, the only requirement for participation in a Master’s course is a basic knowledge of informatics and/or mathematics.

Further information and a detailed description on all courses can be found in our module catalogues here: https://www.wim.uni-mannheim.de/en/student-affairs/module-catalogues/

Business Informatics (Bachelor)

Data Mining (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of data mining. Methodological competence:
  • Successful participants will be able to identify opportunities for applying data mining in an enterprise environment, select and apply appropriate techniques, and interpret the results.
  • project organisation skills

Personal competence:

  • team work skills
  • presentation skills
Recommended requirement:
Literature:
Foundations of Statistics, Practical Informatics I
Examination achievement:
Foundations of Statistics, Practical Informatics I
Instructor(s):
Christian Bizer , Anna Primpeli
Description:
The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:
  • Goals and Principles of Data Mining
  • Data Representation and Preprocessing
  • Clustering
  • Classification
  • Association Analysis
  • Text Mining
  • Systems and Applications (e. g. Retail, Finance, Web Analysis)
Data Security and Privacy (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Frederik Armknecht
Software Engineering Practical (Lecture, English)
Lecture type:
Lecture
ECTS:
5.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Fachkompetenz:
Kenntnisse der Schlüsseltechnologien der modernen Softwaretechnik, sowie der gängigen Software Entwicklungsprozesse. Dies umfasst insbesondere die Gebiete der System- und Anforderungsanalyse, An-wendungsdesign und Systemarchitektur, Implementierung, Validie-rung und Verifikation, Testen, Softwarequalität, Wartung und Wei-terentwicklung von Softwaresystemen.
Methodenkompetenz:
Die Fähigkeit große Softwaresysteme beschreiben, entwerfen und entwickeln zu können unter Berücksichtigung diverser Risiken, die in industriellen Großprojekten auftreten (bspw. Qualität, Kosten, unter-schiedliche Stakeholder, Termindruck, …).
Personale Kompetenz:
Fähigkeiten große Softwaresysteme im Team zu entwerfen, zu entwickeln / implementieren, zu testen und auszuliefern.
Fähigkeiten ein komplexes Themengebiet in schriftlicher und mündlicher Form klar und unmissverständlich wiederzugeben.
Recommended requirement:
Literature:
Programmierpraktikum I, Praktische Informatik I, Programmierprakti-kum II, Algorithmen und Datenstrukturen
Examination achievement:
Programmierpraktikum I, Praktische Informatik I, Programmierprakti-kum II, Algorithmen und Datenstrukturen
Instructor(s):
Arne Lange
Description:
Die Veranstaltung befasst sich mit dem der Methoden und Techniken die für eine team-orientierte, ingenieurmäßige Entwicklung von nicht-trivialen Softwaresystemen erforderlich sind. Insbesondere sind dies:
  • Software-Entwicklungsprozesse
  • System- und Anforderungsanalyse
  • Anwendungsdesign und Systemarchitektur
  • Softwarequalität
  • Validierung, Verifikation und Testen
  • Wartung und Weiterentwicklung
Selected Topics in IT-Security (Lecture w/ Exercise, English)
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
This course aims to increase the security awareness of students and offers them a basic understanding with respect to a variety of interesting topics. After this course, students will be able to (1) learn about symmetric and asymmetric encryption schemes, (2) classify and describe vulnerabilities and protection mechanisms of popular network protocols, web protocols, and software systems (2) analyze / reason about basic protection mechanisms for modern OSs, software and hardware systems.
Recommended requirement:
Literature:
No formal prerequisites. However, knowledge with respect to the content of the following lectures are suggested:
Praktische Informatik I und II, Lineare Algebra, Kenntnisse in Programmierung
Examination achievement:
No formal prerequisites. However, knowledge with respect to the content of the following lectures are suggested:
Praktische Informatik I und II, Lineare Algebra, Kenntnisse in Programmierung
Instructor(s):
Frederik Armknecht
Description:
Background and Learning Objectives
 
The large-scale deployment of Internet-based services and the open nature of the Internet come alongside with the increase of security threats against existing services. As the size of the global network grows, the incentives of attackers to abuse the operation of online applications also increase and their advantage in mounting successful attacks becomes considerable.
 
These cyber-attacks often target the resources, availability, and operation of online services. In the recent years, a considerable number of online services such as Amazon, CNN, eBay, and Yahoo were hit by online attacks; the losses in revenues of Amazon and Yahoo were almost 1.1 million US dollars. With an increasing number of services relying on online resources, security becomes an essential component of every system.
 
Content Description
 
This lecture covers the security of computer, software systems, and tamper resistant hardware. The course starts with a basic introduction on encryption functions, spanning both symmetric and asymmetric encryption techniques, discusses the security of the current encryption standard AES and explains the concept of Zero-Knowledge proofs. The course then continues with a careful examination of wired and wireless network security issues, and web security threats and mechanisms. This part also extends to analysis of buffer overflows. Finally, the course also covers a set of selected security topics such as trusted computing and electronic voting.
 
Topics:
 
  • Encryption Schemes (Private Key vs. Public Key, Block cipher security) and Cryptographic Protocols
  • Cryptanalysis,e.g., side channel attacks
  • Network Security
  • Wireless Security
  • Web Security (SQL, X-Site Scripting)
  • Buffer Overflows
  • Malware & Botnets
  • Trusted computing
  • Electronic Voting
  • OS Security
Software Engineering I (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Fachkompetenz:
Kenntnisse der Schlüsseltechnologien der modernen Softwaretechnik, sowie der gängigen Software Entwicklungsprozesse. Dies umfasst insbesondere die Gebiete der System- und Anforderungsanalyse, Anwendungsdesign und Systemarchitektur, Implementierung, Validierung und Verifikation, Testen, Softwarequalität, Wartung und Weiterentwicklung von Softwaresystemen. Methodenkompetenz:
Die Fähigkeit große Softwaresysteme beschreiben, entwerfen und entwickeln zu können unter Berücksichtigung diverser Risiken, die in industriellen Großprojekten auftreten (bspw. Qualität, Kosten, unterschiedliche Stakeholder, Termindruck, …). Personale Kompetenz:
Fähigkeiten große Softwaresysteme im Team zu entwerfen, zu entwi-ckeln / implementieren, zu testen und auszuliefern.
Fähigkeiten ein komplexes Themengebiet in schriftlicher und mündli-cher Form klar und unmissverständlich wiederzugeben.
Recommended requirement:
Literature:
Praktische Informatik I, Algorithmen und Datenstrukturen, Programmierpraktikum I
Empfohlen: Programmierpraktikum II
Examination achievement:
Praktische Informatik I, Algorithmen und Datenstrukturen, Programmierpraktikum I
Empfohlen: Programmierpraktikum II
Instructor(s):
Colin Atkinson
Description:
Die Veranstaltung befasst sich mit dem Kennenlernen, Verstehen und Anwenden der Methoden, Techniken und Werkzeuge, die für eine team-orientierte, ingenieurmäßige Entwicklung von nicht-trivialen Softwaresystemen erforderlich sind. Insbesondere sind dies:
  • Software-Entwicklungsprozesse
  • System- und Anforderungsanalyse
  • Anwendungsdesign und Systemarchitektur
  • Softwarequalität
  • Validierung, Verifikation und Testen
  • Wartung und Weiterentwicklung

Business Informatics (Master)

Data Mining (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
Students will acquire basic knowledge of the techniques, opportunities and applications of data mining. Methodological competence:
  • Successful participants will be able to identify opportunities for applying data mining in an enterprise environment, select and apply appropriate techniques, and interpret the results.
  • project organisation skills

Personal competence:

  • team work skills
  • presentation skills
Recommended requirement:
Literature:
Foundations of Statistics, Practical Informatics I
Examination achievement:
Foundations of Statistics, Practical Informatics I
Instructor(s):
Christian Bizer , Anna Primpeli
Description:
The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:
  • Goals and Principles of Data Mining
  • Data Representation and Preprocessing
  • Clustering
  • Classification
  • Association Analysis
  • Text Mining
  • Systems and Applications (e. g. Retail, Finance, Web Analysis)
Data Mining and Matrices (Lecture w/ Exercise, English)
Lecture type:
Lecture w/ Exercise
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
Knowledge of the techniques, opportunities, and applications of matrix decompositions in data mining
Methodological competence:
  • Apply matrix decompositions for data mining tasks
  • Analyze and interpret matrix decompositions

Personal competence:

  • writing skills
  • presentation skills
Recommended requirement:
Literature:
IE 500 Data Mining (recommended, not required), basic knowledge of linear algebra
Examination achievement:
IE 500 Data Mining (recommended, not required), basic knowledge of linear algebra
Instructor(s):
Rainer Gemulla
Description:
Many data mining tasks operate on dyadic data, i.e., data involving two types of entities (e.g., users and products, objects and attributes, or points and coordinates); such data can be naturally represented in terms of a matrix. Matrix decompositions, with which we (approximately) represent the data matrix as a product of two (or more) factor matrices, can be used to perform many common data mining tasks. In this lecture, we explore the use of matrix decompositions in data mining, cover data mining tasks such as prediction, clustering and pattern mining, and application areas such as recommender systems and topic modelling.
Data Mining II (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
Students will acquire knowledge of advanced techniques and applications of data mining.
Methodological competence:
  • Successful participants will be able to address advanced issues in data mining projects, conduct complex projects and develop applications in the data mining field.
  • project organization skills

Personal competence:

  • presentation skills
  • team work skills
Recommended requirement:
Literature:
IE 500 Data Mining, programming skills in Java
Examination achievement:
IE 500 Data Mining, programming skills in Java
Instructor(s):
Description:
Data mining deals with the discovery of patterns in data, and with making predictions for the future, based on observations of the past. This course covers advanced issues in data mining which need to be addressed when applying data mining methods in real world projects, including:
  • Data Preprocessing
  • Regression and Forecasting
  • Dimensionality Reduction
  • Anomaly Detection
  • Time Series Analysis
  • Parameter Tuning
  • Ensemble Learning
  • Online Learning
Data Security and Privacy (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Frederik Armknecht
Database Systems II (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Fachkompetenz:
  • Grundlegende Kenntnisse in verteilte relationale Datenbanken
  • objektorientierte Datenbanken
  • objektrelationale Datenbanken
  • deduktive Datenbanken
  • XML-Datenbanken
  • OLAP/OLTP
  • Leistungsbewertung

Methodenkompetenz:

  • Verständnis der alternativen Datenrepräsentationen, deren Vor- und Nachteile
  • Zielorientierter Einsatz der verschiedenen Datenrepräsentationen

Personale Kompetenz:

  • Verständnis der Rolle alternativer Datenmodelle für fundamentale betriebliche Informationssysteme
Recommended requirement:
Literature:
Datenbanksysteme I und deren Voraussetzungen
Examination achievement:
Datenbanksysteme I und deren Voraussetzungen
Instructor(s):
Guido Moerkotte
Description:
Über das relationale Modell hinausgehende Themen (objektorientierte, objektrelationale Datenbanken, SQL/XML).
Information Retrieval and Web Search (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Expertise:
Students will acquire knowledge of fundamental techniques of Information Retrieval and Web Search, including standard retrieval models, evaluation of information retrieval systems, text classification and clustering, as well as web search topics such as crawling and link-based algorithms.
Methodological competence:
Successful participants will be able to understand state-of-the-art methods for Information Retrieval and Web search, as well as being able to select, apply and evaluate the most appropriate techniques for a variety of different search scenarios.
Personal competence:
  • presentation skills;
  • team work skills.
Recommended requirement:
Literature:
Programming skills (Java/C++ preferred). Fundamental notions of linear algebra, probability theory, as well as algorithms and data structures.
Examination achievement:
Programming skills (Java/C++ preferred). Fundamental notions of linear algebra, probability theory, as well as algorithms and data structures.
Instructor(s):
Goran Glavas
Description:
Given the vastness and richness of the Web, users need high-performing, scalable and efficient methods to access its wealth of information and satisfy their information needs. As such, being able to search and effectively retrieve relevant pieces of information from large text collections is a crucial task for the majority (if practically not all) of Web applications. In this course we will explore a variety of basic and advanced techniques for text-based information retrieval and Web search. Covered topics will include:
 
  • Efficient text indexing;
  • Boolean and vector space retrieval models;
  • Evaluation of retrieval systems;
  • Probabilistic Information Retrieval;
  • Text classification and clustering;
  • Web search, crawling and link-based algorithms.
 
Coursework will include homework assignments, a term project and a final exam. Homework assignments are meant to introduce the students to the problems that will be covered in the final exam at the end of the course. In addition, students are expected to successfully complete a term project in teams of 2-4 people. The projects will focus on a variety of IR problems covered in class. Project deliverables include both software (i.e., code and documentation) and a short report explaining the work performed and its evaluation.
Knowledge Graphs Seminar (Seminar, English)
Lecture type:
Seminar
ECTS:
4.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Heiko Paulheim
Knowledge Graphs Seminar (Seminar, English)
Lecture type:
Seminar
ECTS:
4.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Query Optimization (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Learning target:
Fachkompetenz:
  • Verständnis der Grundlegenden Funktionsweisen alternativer Plangeneratoren,
  • detaillierte Kenntnisse physischer Planalternativen,
  • detaillierte Kostenanalysen

Methodenkompetenz:

  • Algorithmen und Komplexitäten der Plangenerierung,
  • Kostenrechnung anhand gegebener Statistiken

Personale Kompetenz:

  • Fundamentales Verständnis für die Probleme und Lösungen der traditionellen Anfragebearbeitung
Recommended requirement:
Literature:
Datenbanksysteme I und deren Voraussetzungen, Kombinatorik, Statistik
Examination achievement:
Datenbanksysteme I und deren Voraussetzungen, Kombinatorik, Statistik
Instructor(s):
Guido Moerkotte
Description:
Grundlagen der Anfrageoptimierung

Business Mathematics (Bachelor)

MAA 508 Advanced Analysis (Lecture, English)
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Recommended requirement:
Literature:
Linear algebra I, Analysis I,II
Examination achievement:
Linear algebra I, Analysis I,II
Instructor(s):
Li Chen
Description:
This course will start with basic knowledge of real analysis, includes measure and integration, then we will go into some advanced topics in analysis, such as L^p spaces, distributions, the Fourier transform, Sobolev spaces and related inequalities. These are necessary knowledge in modern PDE theories and their applications (for example, in physics, biology and economy).
Seminar Graph Theory (Seminar, English)
Lecture type:
Seminar
ECTS:
3.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Instructor(s):
Makiko Mase
Nonparametric Statistics (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Strauch

Business Mathematics (Master)

Lévy Prozesse I (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Instructor(s):
MAA 504 Partial Differential Equations (Lecture, English)
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Learning target:
Fachkompetenz:
Vertrautheit mit den Grundbegriffen partieller Differenzialgleichungen (MK1)
Vertrautheit mit Distributionen, Hölderräumen und Sobolevräumen (MK1)
Vertrautheit mit Sobolevungleichungen (MK1)
Verständnis des Konzepts der schwachen Lösung (MK1, MO2)
Verständnis des Randverhaltens von Lösungen (MK1, MO2)
Methodenkompetenz:
Fähigkeit die Existenz von Lösungen zu untersuchen (MO2)
Fähigkeit die Eindeutigkeit von Lösungen zu untersuchen (MO2)
Fähigkeit die Regularität von Lösungen zu untersuchen (MO2)
Personale Kompetenz:
Vertieftes Verständnis für komplexe Argumentationen in der elliptischen Theorie (MO3)
Recommended requirement:
Literature:
Analysis I, II, Lineare Algebra I, Einführung in die Wahrscheinlichkeitstheorie
Examination achievement:
Analysis I, II, Lineare Algebra I, Einführung in die Wahrscheinlichkeitstheorie
Instructor(s):
Martin Schmidt
Description:
Elliptische Differenzialgleichungen
Funktionenräume
Randwertproblem, Dirichletproblem
Apriori Abschätzungen
MAA 508 Advanced Analysis (Lecture, English)
Lecture type:
Lecture
ECTS:
8.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Hours per week:
4
Recommended requirement:
Literature:
Linear algebra I, Analysis I,II
Examination achievement:
Linear algebra I, Analysis I,II
Instructor(s):
Li Chen
Description:
This course will start with basic knowledge of real analysis, includes measure and integration, then we will go into some advanced topics in analysis, such as L^p spaces, distributions, the Fourier transform, Sobolev spaces and related inequalities. These are necessary knowledge in modern PDE theories and their applications (for example, in physics, biology and economy).
MAC 502 Computational Finance (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0
Course suitable for:
Language of instruction:
English
Instructor(s):
Andreas Neuenkirch
MAC 507 Nonlinear Optimization (Lecture, English)
Lecture type:
Lecture
ECTS:
Course suitable for:
Language of instruction:
English
Hours per week:
2
Instructor(s):
Claudia Schillings , Andreas Sommer
SEM 510 Diffusion Equations (Seminar, English)
Lecture type:
Seminar
ECTS:
Course suitable for:
Language of instruction:
English
Hours per week:
2
Instructor(s):
Li Chen
Seminar Graph Theory (Seminar, English)
Lecture type:
Seminar
ECTS:
3.0
Course suitable for:
Language of instruction:
English
Hours per week:
2
Instructor(s):
Makiko Mase
Nonparametric Statistics (Lecture, English)
Lecture type:
Lecture
ECTS:
6.0 (Modul/e)
Course suitable for:
Language of instruction:
English
Instructor(s):
Claudia Strauch

Contact School of Business Informatics and Mathematics

Juliane Roth, M.A.

Juliane Roth, M.A.

Departmental Exchange Coordinator
University of Mannheim
Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik
B 6, 26
Gebäudeteil B – Room B 1.05
68159 Mannheim
Phone: +49 621 181-2340
Fax: +49 621 181-2423
E-mail: roth(at)wim.uni-mannheim.de
Consultation hour(s):
Wed 10–11:30 a.m. or on appointment Mon–Wed 9 a.m.–3 p.m.