MODULHANDBUCH - iubh-fernstudium.de€¦ · Modul DLMCSITSDP: Cyber Security and Data Protection...

MODULHANDBUCHMaster of Science

Master Computer Science (FS-FI-MACS)

120 ECTS

Fernstudium

Klassifizierung: consecutive

2

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Inhaltsverzeichnis

1. Semester

Modul DLMDSPWP: Programming with PythonModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9Kurs DLMDSPWP01: Programming with Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

Modul DLMCSSESP: Software Engineering: Software ProcessesModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Kurs DLMCSSESP01: Software Engineering: Software Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Modul DLMDSAM: Advanced MathematicsModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Kurs DLMDSAM01: Advanced Mathematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Modul DLMDSAS: Advanced StatisticsModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Kurs DLMDSAS01: Advanced Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Modul DLMBDSA1: Data ScienceModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33Kurs DLMBDSA01: Data Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Modul DLMCSPSE: Project: Software EngineeringModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39Kurs DLMCSPSE01: Project: Software Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

2. Semester

Modul DLMCSA: AlgorithmicsModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Kurs DLMCSA01: Algorithmics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Modul DLMCSITSDP: Cyber Security and Data ProtectionModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55Kurs DLMCSITSDP01: Cyber Security and Data Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Modul DLMCSSCSAS: Seminar: Computer Science and SocietyModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61Kurs DLMCSSCSAS01: Seminar: Computer Science and Society . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

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Modul DLMAIAI: Artificial IntelligenceModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67Kurs DLMAIAI01: Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Modul DLMDSBDT: Big Data TechnologiesModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73Kurs DLMDSBDT01: Big Data Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75

Modul DLMCSPCSP: Project: Computer Science ProjectModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .79Kurs DLMCSPCSP01: Project: Computer Science Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .81

3. Semester

Modul DLMCSSCTCS: Seminar: Current Topics in Computer ScienceModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87Kurs DLMCSSCTCS01: Seminar: Current Topics in Computer Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Modul DLMCSNDS: Networks and Distributed SystemsModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .91Kurs DLMCSNDS01: Networks and Distributed Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

Modul DLMCSEAITSC: Advanced Cyber Security and CryptologyModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .97Kurs DLMCSEAITSC01: Seminar: Advanced Cyber Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Kurs DLMCSEAITSC02: Cryptology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .101

Modul DLMCSEBCQC: Blockchain and Quantum ComputingModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .105Kurs DLMCSEBCQC01: Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .107Kurs DLMCSEBCQC02: Quantum Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .111

Modul DLMBITGSM: IT Governance and Service ManagementModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Kurs DLMBITGSM01: IT Service Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .118Kurs DLMBITGSM02: IT Governance and Compliance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

Modul DLMAIEUIUX: UI/UX ExpertModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125Kurs DLMAIEUIUX01: User Interface and Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Kurs DLMAIEUIUX02: Project: Human Computer Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Modul DLMDSEDE: Data EngineerModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

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Kurs DLMDSEDE01: Data Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135Kurs DLMDSEDE02: Project: Data Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .138

Modul DLMDSEBA: Business AnalystModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141Kurs DLMDSEBA01: Business Intelligence I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Kurs DLMDSEBA02: Project: Business Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .146

Modul DLMDSETPL: Technical Project LeadModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .149Kurs DLMBITPAM01: IT Project Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Kurs DLMDSETPL01: Project: Technical Project Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

Modul DLMCSEMLDL: Machine Learning and Deep LearningModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Kurs DLMDSML01: Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Kurs DLMDSDL01: Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .162

Modul DLMCSEUCIAA: Use Case Identification and Evaluation for Analytical ApplicationsModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .165Kurs DLMDSUCE01: Use Case and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167Kurs DLMDSPDSUC01: Project: Data Science Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

4. Semester

Modul MMTHE: Master ThesisModulbeschreibung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177Kurs MMTHE01: Master Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179Kurs MMTHE02: Colloquium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

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1. Semester

Programming with PythonModule Code: DLMDSPWP

Module Typesee curriculum

Admission RequirementsNone

Study LevelMA

Credit Points5

Semester / Termsee curriculum

Duration Regularly offered inWiSe/SoSe

Language of InstructionEnglisch

Student Workload (in hours)

Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h

Module CoordinatorDr. Reza Shahbazfar

Contributing Courses to Module

▪ Programming with Python (DLMDSPWP01)

Module Exam Type

Module ExamExam

Split Exam

Weight of Modulesee curriculum

Module Contents▪ Introduction to the Python programming language▪ Object-oriented concepts in Python▪ Handling of exceptions and errors▪ The Python library ecosystem▪ Environments and package management▪ Documentation and testing▪ Version control

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Learning OutcomesProgramming with PythonOn successful completion, students will be able to▪ remember basic Python syntax and programming concepts.▪ understand object-oriented concepts in Python.▪ analyze and apply different methods for error handling in Python.▪ know common and important Python libraries and how to apply them to given programming

tasks.▪ understand concepts like environments and version control.

Links to other Modules within the StudyProgramThis module is similar to other modules in thefield of Data Science & Artificial Intelligence.

Links to other Study Programs of IUBHAll Master Programmes in the IT & Technologyfield.

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Programming with PythonCourse Code: DLMDSPWP01

Course TypeOnline Lecture

Study FormatFernstudium

Study LevelMA

Admission Requirementsnone

Field of StudiesData Science & Artificial Intelligence

Credit Points5

Contact Hours (per week) Language of InstructionEnglish


Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h

Information about the examination

Examination Admission Requirements BOLK: yesCourse Evaluation: no

Type of Exam Exam, 90 Minuten

Course DescriptionPython is one of the most versatile and widely used scripting languages. Its clean and unclutteredsyntax as well as its straightforward design greatly contribute to this success and make it an ideallanguage for programming education. Its application ranges from web development to scientificcomputing. Especially in the fields of data science and artificial intelligence, it is the mostcommon programming language supported by all major data-handling and analytical frameworks.This course provides a thorough introduction to the language and its main features, as well asinsights into the rationale and application of important adjacent concepts such as environments,testing, and version control.

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Course OutcomesOn successful completion, students will be able to

▪ remember basic Python syntax and programming concepts.▪ understand object-oriented concepts in Python.▪ analyze and apply different methods for error handling in Python.▪ know common and important Python libraries and how to apply them to given programming

tasks.▪ understand concepts like environments and version control.

Contents1. Introduction to Python

1.1 Data structures1.2 Functions1.3 Flow control1.4 Input / Output1.5 Modules & packages

2. Classes and inheritance2.1 Scopes and namespaces2.2 Classes and inheritance2.3 Iterators and generators

3. Errors and exceptions3.1 Syntax errors3.2 Handling and raising exceptions3.3 User-defined exceptions

4. Important libraries4.1 Standard Python library4.2 Scientific calculations4.3 Speeding up Python4.4 Visualization4.5 Accessing databases

5. Working with Python5.1 Virtual environments5.2 Managing packages5.3 Unit and integration testing5.4 Documenting code

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6. Version control6.1 Introduction to version control6.2 Version control with GIT

Instructional MethodsDie Lehrmaterialien enthalten Skripte, Video-Vorlesungen, Übungen, Podcasts, (Online-) Tutorienund Fallstudien. Sie sind so strukturiert, dass Studierende sie in freier Ortswahl und zeitlichunabhängig bearbeiten können.

Literature

Further Reading▪ Beazley, D., & Jones, B. K. (2013). Python cookbook (3rd ed.). Sebastopol, CA: O’Reilly

Publishing.▪ Barry, P. (2016). Head first Python: A brain-friendly guide (2nd ed.). Sebastopol, CA: O’Reilly

Publishing.▪ Lutz, M. (2013). Learning Python. Sebastopol, CA: O’Reilly Publishing.▪ Ramalho, L. (2015). Fluent Python: Clear, concise, and effective programming. Sebastopol, CA:

O’Reilly Publishing.▪ McKinney, W. (2017). Python for data analysis (2nd ed.). Sebastopol, CA: O’Reilly Publishing.

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DLMDSPWP01

Software Engineering: Software ProcessesModule Code: DLMCSSESP



Study LevelMA

Credit Points5





Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h

Module CoordinatorProf. Dr. Ralf Kneuper


▪ Software Engineering: Software Process (DLMCSSESP01)

Module Exam Type

Module ExamOral Assignment

Split Exam


Module Contents▪ Software process modeling▪ Basic software life cycles▪ Agile and lean processes▪ The Software Product Life Cycle▪ Governance and management of software processes

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Learning OutcomesSoftware Engineering: Software ProcessOn successful completion, students will be able to▪ describe the role of software processes and lifecycle models in software engineering from

initialization to the withdrawal of a software system.▪ describe the notations used for software processes and discuss their relative advantages.▪ discuss the differences and commonalities of plan-driven and agile approaches.▪ select an appropriate process model for specific application cases and discuss their

advantages and disadvantages.▪ adapt (tailor-) selected process models to an individual situation.

Links to other Modules within the StudyProgramThis module is similar to other modules in thefield of Computer Science & SoftwareDevelopment.


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Software Engineering: Software ProcessCourse Code: DLMCSSESP01



Study LevelMA


Field of StudiesComputer Science & Software Development

Credit Points5



Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h



Type of Exam Oral Assignment

Course DescriptionSoftware processes and life cycle models provide a structure for different software engineeringtasks. The aim of this module is to provide an understanding of this structure and how to apply itacross the entire plan-build-run life cycle. An important foundation for working with softwareprocesses is modeling using suitable notation. The different types of life cycles are discussed,including the plan-driven and the agile approaches as well as mixed forms (hybrid models). Aspecial focus is put on the different environments for which these approaches are best suited.Beyond software development, this course also addresses the entire software life cycle, includingthe operations phase and the cooperation between the two phases, e.g., based on DevOps.Software processes are not just a topic on the level of the individual development group orproject but a task for the entire organization; therefore, they should be integrated into overall ITgovernance and management efforts.

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▪ describe the role of software processes and lifecycle models in software engineering frominitialization to the withdrawal of a software system.

▪ describe the notations used for software processes and discuss their relative advantages.▪ discuss the differences and commonalities of plan-driven and agile approaches.▪ select an appropriate process model for specific application cases and discuss their

advantages and disadvantages.▪ adapt (tailor-) selected process models to an individual situation.

Contents1. Foundations of Software Processes

1.1 The Role of Software Processes and Life Cycle Models1.2 Historical Overview

2. Software Process Definition and Modelling2.1 Modelling Notations and Meta-Models2.2 Notations for Modelling the Interaction Between Processes2.3 Detailed-Level Notations

3. Basic Software Product Life Cycle Models3.1 Waterfall Models3.2 The V-Model3.3 Component or Matrix-Based Models3.4 Iterative, Incremental and Evolutionary Development

4. Agile and Lean Development Processes4.1 The Agile Manifesto4.2 Scrum4.3 Common Agile Practices4.4 Kanban and Lean Development4.5 Scaling Agile Development4.6 Hybrid Processes

5. The Software Product Life Cycle5.1 Detailed-Level Process Models: Unified Process and V-Modell XT5.2 IT Service Management and Operations5.3 DevOps5.4 Safety, Security and Privacy

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6. Governance and Management of Software Processes6.1 Process Governance6.2 Process Design and Deployment6.3 Process Tailoring6.4 Process Assessment, Improvement and Measurement6.5 Tool Support

Instructional Methods☑ Course Book☑ Interactive Book☑ Vodcast/Shortcast☑ Podcast☐ Exam Template☐ Review Book☐ Sprint☑ Guidelines☑ Live Tutorials☐ MyCampus Forum☐ Piazza

Literature

Further Reading▪ Boehm, B. (2006). A view of 20th and 21st century software engineering. ICSE '06 proceedings

of the 28th international conference on software engineering. New York, NY: ACM.▪ Boehm, B., & Turner, R. (2003). Balancing agility and discipline: A guide for the perplexed.

Boston, MA: Addison-Wesley/Pearson Education.▪ Kneuper. R. (2018). Software processes and life cycle models. Cham: Springer Nature.▪ Meyer, B. (2014). Agile! The good, the hype and the ugly. Zurich: Springer.▪ Sutherland, J., & Schwaber, K. (2017). The Scrum guideTM. Retrieved from https://

scrumguides.org/

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DLMCSSESP01

Advanced MathematicsModule Code: DLMDSAM



Study LevelMA

Credit Points5





Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h

Module CoordinatorDr. Oksana Zhebel


▪ Advanced Mathematics (DLMDSAM01)

Module Exam Type

Module ExamExam

Split Exam


Module Contents▪ Calculus▪ Integral transformations▪ Vector algebra▪ Vector calculus▪ Matrices and vector spaces▪ Information theory

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Learning OutcomesAdvanced MathematicsOn successful completion, students will be able to▪ remember the fundamental rules of differentiation and integration.▪ apply integration and differentiation techniques to vectors and vector fields.▪ analyze matrix equations.▪ understand the generalization of vectors to tensors.▪ evaluate different metrics from information theoretical perspectives.

Links to other Modules within the StudyProgramThis module is similar to other modules in thefield of Methods.

Links to other Study Programs of IUBHAll Master Programmes in the Business &Management field.

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Advanced MathematicsCourse Code: DLMDSAM01



Study LevelMA


Field of StudiesMethods

Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h




Course DescriptionModern techniques to analyze data and derive predictions for future events are deeply rooted inmathematical techniques.The course builds a solid base to understand the concepts behindadvanced algorithms used to process, analyze, and predict data and observations and enablesstudents to follow future research, especially in the fields of data-intensive sciences.The coursereviews differentiation and integration and then discusses partial differentiation, differentiation,vector algebra and vector calculus. Matrix calculation and vector spaces are fundamental to manymodern data processing algorithms and are discussed in detail. Calculations based on Tensors areintroduced.Common metrics are discussed from an informational, theoretical point of view.

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▪ remember the fundamental rules of differentiation and integration.▪ apply integration and differentiation techniques to vectors and vector fields.▪ analyze matrix equations.▪ understand the generalization of vectors to tensors.▪ evaluate different metrics from information theoretical perspectives.

Contents1. Calculus

1.1 Differentiation & Integration1.2 Partial Differentiation & Integration1.3 Vector Analysis1.4 Calculus of Variations

2. Integral Transformations2.1 Convolution2.2 Fourier Transformation

3. Vector Algebra3.1 Scalars and Vectors3.2 Addition, Subtraction of Vectors3.3 Multiplication of Vectors, Vector Product, Scalar Product

4. Vector Calculus4.1 Integration of Vectors4.2 Differentiation of Vectors4.3 Scalar and Vector Fields4.4 Vector Operators

5. Matrices and Vector Spaces5.1 Basic Matrix Algebra5.2 Determinant, Trace, Transpose, Complex, and Hermitian Conjugates5.3 Eigenvectors and Eigenvalues5.4 Diagonalization5.5 Tensors

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6. Information Theory6.1 MSE6.2 Gini Index6.3 Entropy, Shannon Entropy, Kulback Leibler Distance6.4 Cross Entropy

Instructional Methods☑ Course Book☑ Interactive Book☑ Vodcast/Shortcast☑ Podcast☑ Exam Template☑ Review Book☐ Sprint☐ Guidelines☑ Live Tutorials☐ MyCampus Forum☐ Piazza

Literature

Further Reading▪ Cover, T., & Joy, A. (2006). Elements of information theory (2nd ed.). Hoboken, NJ: John Wiley &

Sons, Inc.▪ McKay, D. (2003). Information theory, inference and learning algorithms. Cambridge:

Cambridge University Press.▪ Riley, K. F., Hobson, M. P., & Bence, S. J. (2006). Mathematical methods for physics and

engineering (3rd ed.). Cambridge: Cambridge University Press.▪ Strang, G. (2016). Introduction to linear algebra. Wellesley, MA: Wellesley-Cambridge Press.

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DLMDSAM01

Advanced StatisticsModule Code: DLMDSAS



Study LevelMA

Credit Points5





Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h

Module CoordinatorN.N.


▪ Advanced Statistics (DLMDSAS01)

Module Exam Type

Module ExamExam

Split Exam


Module Contents▪ Introduction to statistics▪ Important probability distributions and their applications▪ Bayesian statistics▪ Descriptive statistics▪ Data visualization▪ Parameter estimation▪ Hypothesis tests

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Learning OutcomesAdvanced StatisticsOn successful completion, students will be able to▪ understand the fundamental building blocks of statistics.▪ analyze stochastic data in terms of the underlying probability distributions.▪ utilize Bayesian statistics techniques.▪ summarize the properties of observed data using descriptive statistics.▪ apply data visualization techniques to design graphics that illustrate the behavior of

observed data.▪ evaluate model parameters using parameter estimation techniques.▪ create hypothesis tests to discriminate between several model classes.

Links to other Modules within the StudyProgramThis module is similar to other modules in thefield of Methods.

Links to other Study Programs of IUBHAll Master Programmes in the Business &Management field.

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Advanced StatisticsCourse Code: DLMDSAS01



Study LevelMA

Admission RequirementsDLMDSAM01


Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h




Course DescriptionNearly all processes in nature and technical or scientific scenarios are not deterministic butstochastic. Therefore, these processes must be described in terms of probabilities and probabilitydensity distributions.After defining and introducing the fundamental concepts of statistics, thecourse will cover important probability distributions and their prevalence in application scenarios;discuss descriptive techniques to summarize and visualize data effectively; and discuss theBayesian approach to statistics.Estimating parameters is a key ingredient in optimizing datamodels, and the course will give a thorough overview of the most importanttechniques.Hypothesis testing is a crucial aspect in establishing the observation of new effectsand determination of the significance of statistical effects. Special focus will be given to thecorrect interpretation of p-Values and the correct procedure for multiple hypothesis tests.

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▪ understand the fundamental building blocks of statistics.▪ analyze stochastic data in terms of the underlying probability distributions.▪ utilize Bayesian statistics techniques.▪ summarize the properties of observed data using descriptive statistics.▪ apply data visualization techniques to design graphics that illustrate the behavior of

observed data.▪ evaluate model parameters using parameter estimation techniques.▪ create hypothesis tests to discriminate between several model classes.

Contents1. Introduction to Statistics

1.1 Random Variables1.2 Kolmogorov Axioms1.3 Probability Distributions1.4 Decomposing probability distributions1.5 Expectation Values and Moments1.6 Central Limit Theorem1.7 Sufficient Statistics1.8 Problems of Dimensionality1.9 Component Analysis and Discriminants

2. Important Probability Distributions and their Applications2.1 Binomial Distribution2.2 Gauss or Normal Distribution2.3 Poisson and Gamma-Poisson Distribution2.4 Weibull Distribution

3. Bayesian Statistics3.1 Bayes’ Rule3.2 Estimating the Prior, Benford’s Law, Jeffry’s Rule3.3 Conjugate Prior3.4 Bayesian & Frequentist Approach

4. Descriptive Statistics4.1 Mean, Median, Mode, Quantiles4.2 Variance, Skewness, Kurtosis

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5. Data Visualization5.1 General Principles of Dataviz/Visual Communication5.2 1D, 2D Histograms5.3 Box Plot, Violin Plot5.4 Scatter Plot, Scatter Plot Matrix, Profile Plot5.5 Bar Chart

6. Parameter Estimation6.1 Maximum Likelihood6.2 Ordinary Least Squares6.3 Expectation Maximization (EM)6.4 Lasso and Ridge Regularization6.5 Propagation of Uncertainties

7. Hypothesis Test7.1 Error of 1st and 2nd Kind7.2 Multiple Hypothesis Tests7.3 p-Value

Instructional Methods☑ Course Book☑ Interactive Book☑ Vodcast/Shortcast☑ Podcast☑ Exam Template☑ Review Book☐ Sprint☐ Guidelines☑ Live Tutorials☐ MyCampus Forum☐ Piazza

Literature

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Further Reading▪ Bishop, C. (2007). Pattern recognition and machine learning (2nd ed.). Singapore: Springer.▪ Bruce, P., & Bruce, A. (2017). Statistics for data scientists: 50 essential concepts. Sebastopol,

CA: O’Reilley Publishing.▪ Downey, A. (2013). Think Bayes. Sebastopol, CA: O’Reilley Publishing.▪ Downey, A. (2014). Think stats. Sebastopol, CA: O’Reilley Publishing.▪ McKay, D. (2003). Information theory, inference and learning algorithms. Cambridge:

Cambridge University Press.▪ Reinhart, A. (2015). Statistics done wrong. San Francisco, CA: No Starch Press.

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Data ScienceModule Code: DLMBDSA1



Study LevelMA

Credit Points5


DurationMinimaldauer: 1 Semester

Regularly offered inWiSe/SoSe

Language ofInstructionEnglisch


Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h

Module CoordinatorProf. Dr. Ulrich Kerzel


▪ Data Science (DLMBDSA01)

Module Exam Type

Module ExamExam

Split Exam


Module Contents▪ Introduction to data science▪ Use cases and performance evaluation▪ Pre-processing of data▪ Processing of data▪ Selected mathematical techniques▪ Selected artificial intelligence techniques

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Learning OutcomesData ScienceOn successful completion, students will be able to▪ identify use cases and evaluate the performance of data-driven approaches▪ comprehend how data are pre-processed in preparation for analysis.▪ develop typologies for data and ontologies for knowledge representation.▪ decide for appropriate mathematical algorithms to utilize data analysis for a given task.▪ understand the value, applicability, and limitations of artificial intelligence for data analysis.



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Data ScienceCourse Code: DLMBDSA01



Study LevelMA



Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h




Course DescriptionThe course Data Science provides the framework to create value from data. After an introductionthe course covers how to identify suitable use cases and evaluate the performance of data-drivenmethods. The course covers techniques for the technical processing of data and then introducesadvanced mathematical techniques and selected methods from artificial intelligence that areused to analyze data and make predictions.


▪ identify use cases and evaluate the performance of data-driven approaches▪ comprehend how data are pre-processed in preparation for analysis.▪ develop typologies for data and ontologies for knowledge representation.▪ decide for appropriate mathematical algorithms to utilize data analysis for a given task.▪ understand the value, applicability, and limitations of artificial intelligence for data analysis.

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Contents1. Introduction to Data Science

1.1 Overview of Data Science1.2 Terms and Definitions1.3 Applications & Notable Examples1.4 Sources of Data1.5 Structured, Unstructured, Streaming1.6 Typical Data Sources and their Data Type1.7 The 4 V’s of Data: Volume, Variety, Velocity, Veracity1.8 Introduction to Probability Theory1.9 What Are Probabilities and Probability Distributions1.10 Introduction to Bayesian Statistics1.11 Relation to Data Science: Prediction as a Probability

2. Use Cases and Performance Evaluation2.1 Identification of Use Cases for Data Science2.2 Identifying Data Science Use Cases2.3 From Prediction to Decision: Generating Value from Data Science2.4 Evaluation of Predictions2.5 Overview of Relevant Metrics2.6 Business-centric Evaluation: the Role of KPIs2.7 Cognitive Biases and Decision-making Fallacies

3. Pre-processing of Data3.1 Transmission of Data3.2 Data Quality and Cleansing of Data3.3 Transformation of Data (Normalization, Aggregation)3.4 Reduction of Data Dimensionality3.5 Data Visualisation

4. Processing of Data4.1 Stages of Data Processing4.2 Methods and Types of Data Processing4.3 Output Formats of Processed Data

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5. Selected Mathematical Techniques5.1 Linear Regression5.2 Principal Component Analysis5.3 Clustering5.4 Time-series Forecasting5.5 Overview of Further Approaches

6. Selected Artificial Intelligence Techniques6.1 Support Vector Machines6.2 Neural Networks and Deep Learning6.3 Feed-forward Networks6.4 Recurrent Networks and Memory Cells6.5 Convolutional Networks6.6 Reinforcement Learning6.7 Overview of Further Approaches

Instructional Methods☑ Course Book☑ Interactive Book☑ Vodcast/Shortcast☑ Podcast☑ Exam Template☐ Review Book☐ Sprint☐ Guidelines☑ Live Tutorials☐ MyCampus Forum☐ Piazza

Literature

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Further Reading▪ Agrawal, A. (2018). Prediction machines: The simple economics of artificial intelligence.

Brighton, MA: Harvard Business Review.▪ Hu, F. (2016). Big data: storage, sharing, and security. Boca Raton, FL: Auerbach Publications.▪ Ciaburro, G., & Venkateswaran, B. (2017). Neural networks with R: Smart models using CNN,

RNN, deep learning, and artificial intelligence principles. Birmingham: Packt Publishing.▪ Kepner, J., & Jananthan, H. (2018). Mathematics of big data: Spreadsheets, databases,

matrices, and graphs. Cambridge, MA: MIT Press.▪ Russell, S. J., & Norvig, P. (2015). Artificial intelligence: A modern approach. New York, NY:

Pearson Education.▪ Géron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow. Sebastopol,

CA: O’Reilly Media, Inc.

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Project: Software EngineeringModule Code: DLMCSPSE



Study LevelMA

Credit Points5





Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h



▪ Project: Software Engineering (DLMCSPSE01)

Module Exam Type

Module ExamPortfolio

Split Exam


Module Contents▪ The aim of the course is to apply acquired knowledge to a practical scenario. Project teams

work on a project independently over several software process phases.

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Learning OutcomesProject: Software EngineeringOn successful completion, students will be able to▪ draw from first-hand experience working on a complex, practical project for an industrial

software development scenario.▪ identify the risks and typical pitfalls of large software projects and make targeted use of risk-

minimization strategies.▪ apply common techniques in the fields of requirements specification, software design,

implementation, and testing.▪ conduct a comprehensive project documentation.▪ carefully handle project resources.



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Project: Software EngineeringCourse Code: DLMCSPSE01

Course TypeProject


Study LevelMA

Admission RequirementsDLMCSSESP01, DLMDSPWP01


Credit Points5



Self Study120 h

Tutorial30 h

Hours Total150 h


Examination Admission Requirements BOLK: noCourse Evaluation: no

Type of Exam Portfolio

Course DescriptionIn this course, the knowledge students have acquired in prior modules is applied to small- tomedium-sized projects. The concrete implementation takes place in group work. The projectteams go through important stages of the software life cycle and create the appropriate artifacts(e.g., requirements specification, design, implementation, tests, documentation). Quality assuranceof these artifacts is completed by the tutor and students from other project groups. In this way,students learn both the creation and quality assurance of artifacts in a software process.

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▪ draw from first-hand experience working on a complex, practical project for an industrialsoftware development scenario.

▪ identify the risks and typical pitfalls of large software projects and make targeted use of risk-minimization strategies.

▪ apply common techniques in the fields of requirements specification, software design,implementation, and testing.

▪ conduct a comprehensive project documentation.▪ carefully handle project resources.

Contents▪ In software engineering, projects are organized in terms of team member roles (e.g., project

manager, quality assurance) and distinct activities (e.g., planning, analysis, implementation)to create software fitting the customer’s needs. With respect to the chosen approach (agileor plan-driven), students work through all the typical phases relevant for large projects. Theywill gain experience regarding organizational concerns like common roles and activities, aswell as practical concerns like the creation of requirements specification, design,implementation, tests, and documentation.

Instructional Methods☐ Course Book☐ Interactive Book☐ Vodcast/Shortcast☐ Podcast☐ Exam Template☐ Review Book☐ Sprint☑ Guidelines☑ Live Tutorials☐ MyCampus Forum☐ Piazza

Literature

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Further Reading▪ Bass, L., Clements, P., & Kazman, R. (2012). Software architecture in practice (3rd ed.). Boston,

MA: Addison-Wesley.▪ Gruhn, V., & Striemer, R. (Eds.). (2018). The essence of software engineering. Cham: Springer

Open.▪ Martin, R. C. (2008). Clean code: A handbook of agile software craftsmanship. Boston, MA:

Pearson.▪ Martin, R. C. (2017). Clean architecture: A craftsman's guide to software structure and design.

Boston, MA: Prentice Hall.▪ Pohl, K., & Rupp, C. (2015). Requirements engineering fundamentals (2nd ed.). San Rafael, CA:

Rocky Nook.▪ Sommerville, I. (2016). Software engineering (10th ed.). Boston, MA: Pearson.

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DLMCSPSE01

2. Semester

AlgorithmicsModule Code: DLMCSA



Study LevelMA

Credit Points5





Self Study120 h

Tutorial30 h

Hours Total150 h



▪ Algorithmics (DLMCSA01)

Module Exam Type

Module ExamExam

Split Exam


Module Contents▪ Algorithm design▪ Introduction to important classes of algorithms▪ Correctness and completeness of algorithms▪ Computability and the theoretical limits of algorithms▪ Efficiency of algorithms

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Learning OutcomesAlgorithmicsOn successful completion, students will be able to▪ explain the concept of algorithms and its relevance in computing.▪ evaluate the correctness of software programs.▪ discuss the theoretical and practical limits of software programs.▪ select algorithms to solve specific application problems.▪ design new algorithms based on standard methods to solve simple application problems.▪ analyze and compare algorithms and outline their strengths and weaknesses.



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AlgorithmicsCourse Code: DLMCSA01



Study LevelMA



Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h




Course DescriptionA core activity of computer science and related disciplines is the design, use, and application ofalgorithms to solve problems. This course introduces common approaches to the design ofalgorithms, as well as important classes of algorithms that can be used to solve commonproblems. Performing these tasks successfully requires a thorough understanding of the qualitycharacteristics of algorithms: (partial and total) correctness, accuracy, completeness, andefficiency. At the same time, there are limits to what an algorithm can and should achieve, intheory and in practice, and it is important to realize these limits and take them into account.Apart from algorithms based on standard programming paradigms, there are also various otherprogramming paradigms that lead to other types of algorithms; therefore, this course alsoprovides a short introduction to algorithms for parallel computing, probabilistic algorithms, andquantum algorithms.

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▪ explain the concept of algorithms and its relevance in computing.▪ evaluate the correctness of software programs.▪ discuss the theoretical and practical limits of software programs.▪ select algorithms to solve specific application problems.▪ design new algorithms based on standard methods to solve simple application problems.▪ analyze and compare algorithms and outline their strengths and weaknesses.

Contents1. Introduction to Algorithms

1.1 Basic Concepts and Historical Overview1.2 Algorithms, Programming Languages and Data Structures1.3 Quality Algorithms: Correctness, Accuracy, Completeness, Efficiency1.4 The Role of Algorithms in Society

2. Algorithm Design2.1 Data Structures2.2 Recursion and Iteration2.3 Divide-and-Conquer2.4 Balancing, Greedy Algorithms, and Dynamic Programming

3. Some Important Algorithms3.1 Sorting and Searching3.2 Pattern-Matching3.3 The RSA Algorithm3.4 The k-Means Algorithm for Data Clustering

4. Correctness, Accuracy, and Completeness of Algorithms4.1 Partial Correctness4.2 Total Correctness4.3 Ensuring Correctness in Day-to-Day Programming4.4 Accuracy, Approximation, and Error Analysis

5. Computability5.1 Models of Computation5.2 The Halting Problem5.3 Undecidable Problems

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6. Efficiency of Algorithms: Complexity Theory6.1 Models of Complexity6.2 NP-Completeness6.3 P=NP?

7. Advanced Algorithmics7.1 Parallel Computing7.2 Probabilistic Algorithms


Literature

Further Reading▪ Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2013). Introduction to algorithms (3rd

ed.). Cambridge, MA: MIT Press.▪ Dewdney, A. K. (2001). The new turing omnibus. London: Macmillan Education.▪ Harel, D. (2014). Algorithmics: The spirit of computing (3rd ed.). Berlin, Heidelberg: Springer.▪ Sedgewick, A., & Wayne, K. (2011). Algorithms (4th ed.). Boston, MA: Pearson Education.▪ Skiena, S. S. (2012). The algorithm design manual (2nd ed.). London: Springer.

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DLMCSA01

Cyber Security and Data ProtectionModule Code: DLMCSITSDP



Study LevelMA

Credit Points5





Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h



▪ Cyber Security and Data Protection (DLMCSITSDP01)

Module Exam Type


Split Exam


Module Contents▪ Data protection and privacy▪ Cyber security building blocks▪ Cyber security management▪ Cryptography concepts▪ Cryptography applications

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Learning OutcomesCyber Security and Data ProtectionOn successful completion, students will be able to▪ explain the core concepts of cyber security, data protection, and cryptography including their

differences and relationships.▪ compare the approaches to data protection within in different legal systems.▪ apply data protection concepts to data science and other application scenarios.▪ analyze application scenarios to identify the adequate cyber security management measures

that should be implemented.



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Cyber Security and Data ProtectionCourse Code: DLMCSITSDP01



Study LevelMA



Credit Points5



Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h




Course DescriptionWith the increasing digitization and networking of IT systems, the need for safeguarding systemsand the data processed by these systems has grown. The aim of this module is to provide anunderstanding of security measures needed, cyber security including cryptography, and dataprotection. While the need for cyber security is similar around the world, different cultures havedifferent expectations regarding data protection and privacy. Nevertheless, personal data areoften processed outside the country where the affected individuals live. Hence, the culturalaspects of data protection need to be taken into account wherever the data are processed. Thiscourse provides an overview of the main cyber security measures in different applicationscenarios, as well as their integration into an Information Security Management System, withparticular focus on the relevant ISO/IEC 270xx family of standards. Cryptography provides animportant tool set for cyber security and is used in many different application scenarios such assecure Internet protocols and block chain.

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▪ explain the core concepts of cyber security, data protection, and cryptography including theirdifferences and relationships.

▪ compare the approaches to data protection within in different legal systems.▪ apply data protection concepts to data science and other application scenarios.▪ analyze application scenarios to identify the adequate cyber security management measures

that should be implemented.

Contents1. Foundations of Data Protection and Cyber Security

1.1 Terminology and Risk Management1.2 Core Concepts of Cyber Security1.3 Core Concepts of Data Protection and Privacy1.4 Core Concepts of Cryptography1.5 Legal Aspects

2. Data Protection2.1 Basic Concepts of Data Protection (ISO/IEC 29100, Privacy by Design)2.2 Data Protection in Europe: the GDPR2.3 Data Protection in the USA2.4 Data Protection in Asia

3. Applying Data Protection3.1 Anonymity and Pseudonyms (k-Anonymity, i-Diversity, Differential Privacy)3.2 Data Protection in Data Science and Big Data3.3 User Tracking in Online Marketing3.4 Cloud Computing

4. Building Blocks of Cyber Security4.1 Authentication, Access Management and Control4.2 Cyber Security in Networks4.3 Developing Secure IT Systems (OWASP, etc.)

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5. Cyber Security Management5.1 Security Policy5.2 Security and Risk Analysis5.3 The ISO 270xx Series5.4 IT Security and IT Governance5.5 Example: Cyber Security for Credit Cards (PCI DSS)

6. Cryptography6.1 Symmetric Cryptography6.2 Asymmetric Cryptography6.3 Hash Functions6.4 Secure Data Exchange (Diffie-Hellman, Perfect Forward Secrecy, etc.)

7. Cryptographic Applications7.1 Digital Signatures7.2 Electronic Money7.3 Secure Internet Protocols (TLS, IPSec, etc.)7.4 Block Chain


Literature

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Further Reading▪ Bowman, C., Gesher, A., Grant, J., & Slate, D. (2015). The architecture of privacy: On engineering

technologies that can deliver trustworthy safeguards. Sebastopol, CA: O'Reilly.▪ Hintzbergen, J., Hintzbergen, K., Smulders, A., & Baars, H. (2015). Foundations of information

security (3rd ed.). Zaltbommel: Van Haren Publishing.▪ ISO/IEC 29100. (2011). Information technology — Security techniques — Privacy framework.

ISO. Retrieved from https://standards.iso.org/ittf/PubliclyAvailableStandards/c045123_ISO_IEC_29100_2011.zip

▪ Paar, C., & Pelzl, J. (2011). Understanding cryptography: A textbook for students andpractitioners. Heidelberg: Springer.

▪ The Open Web Application Security Project (OWASP). (2005). A guide to building secure webapplications and web services. OWASP. Retrieved from https://www.um.es/atica/documentos/OWASPGuide2.0.1.pdf

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Seminar: Computer Science and SocietyModule Code: DLMCSSCSAS



Study LevelMA

Credit Points5





Self Study120 h

Tutorial30 h

Hours Total150 h

Module CoordinatorProf. Dr. André Köhler


▪ Seminar: Computer Science and Society (DLMCSSCSAS01)

Module Exam Type

Module ExamWritten Assessment: Research Essay

Split Exam


Module Contents▪ The seminar covers the relationship between computer science and society, including topics

such as the social responsibility of computer scientists and the effects of digitization onsociety. Based on a list of topics updated regularly, students select or are assigned a specifictopic on which to write a scientific report.

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Learning OutcomesSeminar: Computer Science and SocietyOn successful completion, students will be able to▪ name the main questions regarding the relationship between computer science and society.▪ discuss selected topics regarding the relationship between computer science and society.▪ analyze one aspect of the relationship between computer science and society in detail.▪ take selected topics and case studies and link them with well-known concepts, as well as

critically question and discuss them.▪ transfer theoretically acquired knowledge to a specific context.▪ edit scientifically a select topic.



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Seminar: Computer Science and SocietyCourse Code: DLMCSSCSAS01

Course TypeSeminar


Study LevelMA



Credit Points5



Self Study120 h

Tutorial30 h

Hours Total150 h



Type of Exam Written Assessment: Research Essay

Course DescriptionThe seminar covers the relationship between computer science and society. Over the past severaldecades, computer science has greatly changed society, and it is important that prospectivecomputer scientists think about the effects of computer science on society and take theseinfluences into account in their work. Typical topics to be addressed include, for example, theeffects of ethics and professionalism in computing, the responsibility of computer scientists, theeffects of data science and social networks on society, surveillance, and dual use of IT.

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▪ name the main questions regarding the relationship between computer science and society.▪ discuss selected topics regarding the relationship between computer science and society.▪ analyze one aspect of the relationship between computer science and society in detail.▪ take selected topics and case studies and link them with well-known concepts, as well as

critically question and discuss them.▪ transfer theoretically acquired knowledge to a specific context.▪ edit scientifically a select topic.

Contents▪ The seminar covers different topics regarding the relationship between computer science

and society. Each participant must create a seminar paper on a topic assigned to him/herand present the contents of the written paper.

Instructional Methods☐ Course Book☐ Interactive Book☐ Vodcast/Shortcast☐ Podcast☐ Exam Template☐ Review Book☐ Sprint☑ Guidelines☐ Live Tutorials☐ MyCampus Forum☐ Piazza

Literature

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Further Reading▪ Association for Computing Machinery (ACM). (2018). ACM Code of Ethics and Professional

Conduct. Retrieved from https://www.acm.org/code-of-ethics▪ Barger, R. N. (2008). Computer ethics: A case-based approach. Cambridge: Cambridge

University Press.▪ Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the

poor. New York, NY: St. Martin's Press.▪ Gay, J., Stallman, R. M., & Lessig, L. (2002). Free software, free society: Selected essays of

Richard M. Stallman. Boston, MA: GNU Press.▪ Greenwald, G. (2014). No place to hide. London: Penguin.▪ Murray, A. (2019). Information technology law: The law and society (4th ed.). Oxford: Oxford

University Press.▪ Pariser, E. (2011). The filter bubble. What the internet is hiding from you. London: Penguin.▪ Weckert, J. (Ed.). (2016). Computer ethics. Abingdon: Routledge.

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DLMCSSCSAS01

Artificial IntelligenceModule Code: DLMAIAI



Study LevelMA

Credit Points5





Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h

Module CoordinatorProf. Dr. UIrich Kerzel


▪ Artificial Intelligence (DLMAIAI01)

Module Exam Type

Module ExamExam

Split Exam


Module Contents▪ History of AI▪ AI application areas▪ Expert systems▪ Neuroscience▪ Modern AI systems

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Learning OutcomesArtificial IntelligenceOn successful completion, students will be able to▪ remember the historical developments in the field of artificial intelligence.▪ analyze the different application areas of artificial intelligence.▪ comprehend expert systems.▪ apply Prolog to simple expert systems.▪ comprehend the brain and cognitive processes from a neuro-scientific point of view.▪ understand modern developments in artificial intelligence.


Links to other Study Programs of IUBHAll Bachelor Programmes in the IT & Technologyfield.

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Artificial IntelligenceCourse Code: DLMAIAI01



Study LevelMA



Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h




Course DescriptionThe quest for artificial intelligence has captured humanity’s interest for many decades and hasbeen an active research area since the 1960s. This course will give a detailed overview of thehistorical developments, successes, and set-backs in AI, as well as the development and use ofexpert systems in early AI systems.In order to understand cognitive processes, the course will givea brief overview of the biological brain and (human) cognitive processes and then focus on thedevelopment of modern AI systems fueled by recent developments in hard- and software.Particular focus will be given to discussion of the development of “narrow AI” systems for specificuse cases vs. the creation of general artificial intelligence.The course will give an overview of awide range of potential application areas in artificial intelligence, including industry sectors suchas autonomous driving and mobility, medicine, finance, retail, and manufacturing.

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▪ remember the historical developments in the field of artificial intelligence.▪ analyze the different application areas of artificial intelligence.▪ comprehend expert systems.▪ apply Prolog to simple expert systems.▪ comprehend the brain and cognitive processes from a neuro-scientific point of view.▪ understand modern developments in artificial intelligence.

Contents1. History of AI

1.1 Historical Developments1.2 AI Winter1.3 Notable Advances in AI

2. Expert Systems2.1 Overview Over Expert Systems2.2 Introduction to Prolog

3. Neuroscience3.1 The (Human) Brain3.2 Cognitive Processes

4. Modern AI Systems4.1 Recent Developments in Hard- and Software4.2 Narrow vs General AI4.3 NLP and Computer Vision

5. AI Application Areas5.1 Autonomous Vehicles & Mobility5.2 Personalized Medicine5.3 FinTech5.4 Retail & Industry

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Literature

Further Reading▪ Bear, F., Barry, W., & Paradiso, M. (2006). Neuroscience: Exploring the brain (3rd ed.). Baltimore,

MD: Lippincott Williams and Wilkins.▪ Bratko, I. (2011). Prolog programming for artificial intelligence (4th ed.). Hoboken, NJ: Pearson.▪ Jackson, P. (1998). Introduction to expert systems (3rd ed.). Chicago, IL: Addison Wesley

Longman.▪ Nilsson, N. (2009). The quest for artificial intelligence. Cambridge: Cambridge University Press.▪ Russel, S., & Norvig, P. (2009). Artificial intelligence: A modern approach (3rd ed.). Malaysia:

Pearson.

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DLMAIAI01

Big Data TechnologiesModule Code: DLMDSBDT



Study LevelMA

Credit Points5





Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h

Module CoordinatorProf. Dr. Thomas Zöller


▪ Big Data Technologies (DLMDSBDT01)

Module Exam Type


Split Exam


Module Contents▪ Data types and data sources▪ Databases▪ Modern storage frameworks▪ Data formats▪ Distributed computing

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Learning OutcomesBig Data TechnologiesOn successful completion, students will be able to▪ identify the different types and sources of data.▪ understand different database concepts.▪ build new database structures.▪ evaluate various data storage frameworks w.r.t. project requirements.▪ analyze which data format to use for a given project.▪ create a distributed computing environment for a given project.



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Big Data TechnologiesCourse Code: DLMDSBDT01

Course TypeOnline-Vorlesung


Study LevelMA



Credit Points5



Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h


Examination Admission Requirements BOLK: yesCourse Evaluation: yes


Course DescriptionData are often considered the “new oil”, the raw material from which value is created. To harnessthe power of data, the data need to be stored and processed on a technical level. This courseintroduces the four “Vs” of data, as well as typical data sources and types. This course thendiscusses how data are stored in databases. Particular focus is given to database structures anddifferent types of databases, e.g., relational, noSQL, NewSQL, and time-series.Beyond classical andmodern databases, this course covers a wide range of storage frameworks such as distributedfilesystems, streaming, and query frameworks. This is complemented by a detailed discussion ofdata storage formats ranging from classical approaches such as CSV and HDF5 to more modernapproaches like Apache Arrow and Parquet.Finally, this course gives an overview of distributedcomputing environments based on local clusters, cloud computing facilities, and container-basedapproaches.

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▪ identify the different types and sources of data.▪ understand different database concepts.▪ build new database structures.▪ evaluate various data storage frameworks w.r.t. project requirements.▪ analyze which data format to use for a given project.▪ create a distributed computing environment for a given project.

Contents1. Data Types and Data Sources

1.1 The 4Vs of data: volume, velocity, variety, veracity1.2 Data sources1.3 Data types

2. Databases2.1 Database structures2.2 Introduction to SQL2.3 Relational databases2.4 nonSQL, NewSQL databases2.5 Timeseries DB

3. Modern data storage frameworks3.1 Distributed Filesystems3.2 Streaming frameworks3.3 Query frameworks

4. Data formats4.1 Traditional data exchange formats4.2 Apache Arrow4.3 Apache Parquet

5. Distributed Computing5.1 Cluster-based approaches5.2 Containers5.3 Cloud-based approaches

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Literature

Further Reading▪ Date, C. J. (2012). Database design and relational theory: Normal forms and all that jazz.

Sebastopol, CA: O’Reilly Publishing.▪ Karau, H., Konwinski, A., Wendell, A., & Zaharia, M. (2015). Learning spark: Lightning-fast data

analysis. Sebastopol, CA: O’Reilly Publishing.▪ Narkhede, N., Shapira, G., & Palino, T. (2017). Kafka: The definitive guide: Real-time data and

stream processing at scale. Sebastopo, CA: O’Reilly Publishing.▪ Poulton, N. (2017). Docker deep dive. Nigel Poulton.▪ Psaltis, A. (2017). Streaming data: Understanding the real-time pipeline. Shelter Island, NY:

Manning Publications.▪ Redmond, E., & Wilson, J. R. (2012). Seven databases in seven weeks: A guide to modern

databases and the noSQL movement. Dallas, TX: Pragmatic Bookshelf.▪ Sadalage, P., & Fowler, M. (2012). NoSQL distilled: A brief guide to the emerging world of

polyglot persistence. Ann Arbor, MI: Addison-Wesley.▪ Viescas, J., & Hernandez, M. (2014). SQL queries for mere mortals: A hands-on guide to data

manipulation in SQL, (3rd ed.). Ann Arbor, MI: Addison-Wesley.▪ White, T. (2015). Hadoop: The definitive guide: Storage and analysis at Internet scale.

Sebastopol, CA: O’Reilly Publishing.

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DLMDSBDT01

Project: Computer Science ProjectModule Code: DLMCSPCSP



Study LevelMA

Credit Points5





Self Study120 h

Tutorial30 h

Hours Total150 h



▪ Project: Computer Science Project (DLMCSPCSP01)

Module Exam Type

Module ExamPortfolio

Split Exam


Module Contents▪ In this course, students learn to apply computer science concepts they have learned in

previous modules to a real-world project.

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Learning OutcomesProject: Computer Science ProjectOn successful completion, students will be able to▪ apply the concepts of computer science to real-world problems.▪ translate learned theories into practice in the field of computer science.▪ analyze a real-world problem and design a computer science solution for it.▪ explain their portfolio work and its scientific background.▪ produce adequate documentation for their work.



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Project: Computer Science ProjectCourse Code: DLMCSPCSP01

Course TypeProjekt


Study LevelMA


Field of StudiesInformatik & Software-Entwicklung

Credit Points5



Self Study120 h

Tutorial30 h

Hours Total150 h


Examination Admission Requirements BOLK: noCourse Evaluation: yes


Course DescriptionStudents will define a portfolio project in which they will be able to apply the knowledge in thefield of computer science they have learned in previous courses. Students create the portfolioproject and document the results, reflecting on applied computer science concepts and theirinfluence on the success of the portfolio project.


▪ apply the concepts of computer science to real-world problems.▪ translate learned theories into practice in the field of computer science.▪ analyze a real-world problem and design a computer science solution for it.▪ explain their portfolio work and its scientific background.▪ produce adequate documentation for their work.

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Contents▪ In this course, students create a portfolio in which they apply the topics covered in the

previous modules in this degree program.


Literature

Further Reading▪ Hintzbergen, J., Hintzberger, K., Smulders, A., & Baars, H. (2015). Foundations of information

security. Based on ISO 27001 and ISO 27002. Zaltbommel: Van Haren Publishing.▪ Phillips, J. (2010). IT project management: On track from start to finish (3rd ed.). New York, NY:

McGraw-Hill.▪ Sedgewick, R., & Wayne, K. (2016). Computer science: An interdisciplinary approach. Boston,

MA: Addison-Wesley.

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3. Semester

Seminar: Current Topics in Computer ScienceModule Code: DLMCSSCTCS



Study LevelMA

Credit Points5





Self Study120 h

Tutorial30 h

Hours Total150 h



▪ Seminar: Current Topics in Computer Science (DLMCSSCTCS01)

Module Exam Type

Module ExamWritten Assessment: Research Essay

Split Exam


Module Contents▪ This seminar deals with current topics of computer science. Students make a dive deep into

a specific topic within a sub-discipline of their choice. These disciplines are including but notlimited to the following: artificial intelligence, big data technologies, IT security and dataprotection, algorithmics, data science, and programming.

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Learning OutcomesSeminar: Current Topics in Computer ScienceOn successful completion, students will be able to▪ name current and relevant topics in the sub-disciplines of computer science.▪ give examples of the influence of computer science on current developments.▪ transfer theoretical knowledge to real case studies.▪ translate learned theories into practical application in the computer science field.▪ write about a select computer science topic in a scientific manner.▪ critically question and discuss current problems in computer science.▪ adapt sub-discipline-specific models and frameworks for use in solving practical problems.



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Seminar: Current Topics in Computer ScienceCourse Code: DLMCSSCTCS01

Course TypeSeminar


Study LevelMA



Credit Points5



Self Study120 h

Tutorial30 h

Hours Total150 h




Course DescriptionThis seminar is an opportunity for students to deepen the broad knowledge they will have gainedover the previous four semesters of the study program. Students will choose a topic of specificindividual interest that is connected to a sub-discipline of computer science. If a student, forexample, is interested in the application of artificial intelligence in a specific context, elaboratingcontext-specific use cases from a literature review can be the theme of the essay. Feedbackprovided by the tutor will help students strengthen any weaknesses they may have in scientificwriting and academic work and prepare students for writing their master thesis.

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▪ name current and relevant topics in the sub-disciplines of computer science.▪ give examples of the influence of computer science on current developments.▪ transfer theoretical knowledge to real case studies.▪ translate learned theories into practical application in the computer science field.▪ write about a select computer science topic in a scientific manner.▪ critically question and discuss current problems in computer science.▪ adapt sub-discipline-specific models and frameworks for use in solving practical problems.

Contents▪ Computer science is a broad subject area with many very different facets, depending on the

specific sub-discipline. This seminar will address this diversity by taking up current trends inthe context of individually-prepared texts. Each participant must create an essay for thispurpose. Possible topics include artificial intelligence, big data technologies, IT security anddata protection, algorithmics, data science, and programming.


Literature

Further Reading▪ Brookshear, G., & Bylow, D. (2014). Computer science: An overview (12th ed.). Boston, MA:

Pearson.▪ Gruhn, V., & Striemer, R. (Eds.). (2018). The essence of software engineering. Cham: Springer.▪ Springer. (n.d.) Lecture Notes in Computer Science. Heidelberg: Springer.▪ Tardos, E. (Ed.). (n.d.) Journal of the ACM.

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Networks and Distributed SystemsModule Code: DLMCSNDS



Study LevelMA

Credit Points5





Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h

Module CoordinatorProf. Dr. Carsten Clauß


▪ Networks and Distributed Systems (DLMCSNDS01)

Module Exam Type

Module ExamExam

Split Exam


Module Contents▪ Communication Networks▪ Communication Protocols▪ Distributed System Architectures▪ Distributed Algorithms and Applications

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Learning OutcomesNetworks and Distributed SystemsOn successful completion, students will be able to▪ explain the basic concepts of digital data transmission and computer networks.▪ detail the ISO/OSI reference model and characterize aspects of its different layers.▪ compare the ISO/OSI model to the TCP/IP protocol stack, its services, and its applications.▪ elaborate on different approaches and architectures for distributed systems.▪ describe the challenges and opportunities of distributed algorithms and applications.▪ analyze different aspects of decentralized, mobile, and pervasive computing.



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Networks and Distributed SystemsCourse Code: DLMCSNDS01



Study LevelMA



Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h




Course DescriptionIsolated computer systems are becoming the exception, with modern systems typically connectedto each other via networks. Through these networks, data is constantly exchanged via the internetusing communication protocols. These allow modern computers to access data and functionsfrom other computer systems, enabling distributed systems in which algorithms and applicationsare partially mapped to different entities within the network to perform shared computing tasks.The knowledge transfer regarding the required technologies, architectures, and algorithms fordoing so is the focus of this course.

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▪ explain the basic concepts of digital data transmission and computer networks.▪ detail the ISO/OSI reference model and characterize aspects of its different layers.▪ compare the ISO/OSI model to the TCP/IP protocol stack, its services, and its applications.▪ elaborate on different approaches and architectures for distributed systems.▪ describe the challenges and opportunities of distributed algorithms and applications.▪ analyze different aspects of decentralized, mobile, and pervasive computing.

Contents1. Computer Networks

1.1 Basic Concepts of Digital Data Transmission1.2 Network Topologies and Interconnections1.3 Basics of Communication Engineering and Coding Theory1.4 The Physical Layer: Transmission Methods and Media

2. Communication Protocols2.1 The ISO/OSI Reference Model2.2 The Data Link Layer: Standards and Technologies2.3 The Network Layer: Addressing and Routing2.4 The Transport Layer: Reliability and Flow Control

3. The Internet Protocol Suite3.1 History of the Internet and the World Wide Web3.2 The TCP/IP Reference Model and Protocol Stack3.3 Examples of Internet Protocols and Services3.4 Security Aspects of Communication on the Internet

4. Architectures of Distributed Systems4.1 Client-Server Architectures4.2 Service-Oriented Architectures, Web- and Micro-Services4.3 Edge and Cloud Computing4.4 Peer-to-Peer Computing

5. Distributed Algorithms and Applications5.1 Communication and Synchronization in Distributed Systems5.2 Distributed Algorithms (Concurrency and Parallel Processing)5.3 Transactions and Data Management (Consistency and Replication)5.4 Security Aspects for Distributed Services and Applications

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6. From Distributed Systems to Ubiquitous Computing6.1 Aspects of Decentralized Applications (Distributed Ledger Technology)6.2 Aspects of Mobile Computing (Networks, Protocols and Applications)6.3 Aspects of Pervasive Computing and the Internet of Things


Literature

Further Reading▪ Tanenbaum, A. S., & Wetherall, D. J. (2014). Computer networks: New international edition (5th

ed.). Harlow, Essex: Pearson Education.▪ Comer, D. E. (2015). Computer networks and internets, global edition (6th ed.). Harlow, Essex:

Pearson Education.▪ Kurose, J., & Keith R. (2017). Computer networking: A top-down approach, global edition (7th

ed.). Harlow, Essex: Pearson Education.▪ Comer, D. E. (2018). The internet Book: Everything you need to know about computer

networking and how the internet works (5th ed.). Boca Raton, FL: CRC Press.▪ Van Steen, M., & Tanenbaum, A. S. (2017). Distributed systems (3rd ed.). CreateSpace

Independent Publishing Platform.

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DLMCSNDS01

Advanced Cyber Security and CryptologyModule Code: DLMCSEAITSC



Study LevelMA

Credit Points10





Self Study230 h

Tutorial50 h

Self Test20 h

Hours Total150 h

Module CoordinatorDr. Alexander Lawall / Dr. Rolf Kueres


▪ Seminar: Advanced Cyber Security (DLMCSEAITSC01)▪ Cryptology (DLMCSEAITSC02)

Module Exam Type

Module Exam Split ExamSeminar: Advanced Cyber SecurityWritten Assessment: Research Essay ( 50)

CryptologyFachpräsentation ( 50)


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Module Contents

Seminar: Advanced Cyber Security▪ This course covers selected advanced topics in cyber security, including the closely related

topics of data protection and cryptology, and discusses them in detail. Based on a list oftopics updated regularly, students select or are assigned a specific topic about which theywrite a scientific research essay.

Cryptology▪ Symmetric and asymmetric cryptosystems▪ Authentication▪ Cryptanalysis▪ Cryptology in the internet▪ Applications

Learning OutcomesSeminar: Advanced Cyber SecurityOn successful completion, students will be able to▪ analyze and describe one aspect of cyber security in detail.▪ independently analyze selected topics in cyber security and link them with well-known

concepts, as well as critically question and discuss them.▪ transfer theoretically-acquired knowledge to a specific context.▪ write and edit a scientific essay on a relevant select topic.

CryptologyOn successful completion, students will be able to▪ discuss the main cryptographic systems and algorithms and their relevance in IT today.▪ discuss the security of internet-based applications.▪ evaluate different cryptographic systems and algorithms to select an appropriate solution for

real-world problems in IT.▪ apply standard cryptographic systems and algorithms to solve real-world problems in IT.▪ appraise existing cryptographic solutions to real-world problems and identify major

weaknesses where relevant.



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Seminar: Advanced Cyber SecurityCourse Code: DLMCSEAITSC01

Course TypeSeminar


Study LevelMA

Admission RequirementsDLMCSITSDP01 or DLMCSITSDS01


Credit Points5



Self Study120 h

Tutorial30 h

Hours Total150 h




Course DescriptionThis seminar covers advanced topics in cyber security. With the growth of the internet anddigitization, cyber security has become an increasingly important topic and needs to be taken intoaccount in the development and setup of software and IT systems. Typical topics that may beaddressed include the analysis of selected aspects of information security management systemsaccording to the ISO 27000 series; the use of cyber security to support data protection; and thedetailed analysis and description of certain algorithms or cryptosystems.


▪ analyze and describe one aspect of cyber security in detail.▪ independently analyze selected topics in cyber security and link them with well-known

concepts, as well as critically question and discuss them.▪ transfer theoretically-acquired knowledge to a specific context.▪ write and edit a scientific essay on a relevant select topic.

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Contents▪ The seminar covers different advanced topics regarding cyber security. Each participant must

prepare a research essay on a topic assigned to him/her.


Literature

Further Reading▪ Bowman, C., Gesher, A., Grant, J., & Slate, D. (2015). The architecture of privacy: On engineering

technologies that can deliver trustworthy safeguards. Sebastopol, CA: O'Reilly.▪ Hintzbergen, J., Hintzbergen, K., Smulders, A., & Baars, H. (2015). Foundations of information

security (3rd ed.). Zaltbommel: Van Haren Publishing.▪ ISO/IEC 29100. (2011). Information technology — Security techniques — Privacy framework.

ISO. Retrieved from https://standards.iso.org/ittf/PubliclyAvailableStandards/c045123_ISO_IEC_29100_2011.zip

▪ Paar, C., & Pelzl, J. (2011). Understanding cryptography: A textbook for students andpractitioners. Heidelberg: Springer.

▪ The Open Web Application Security Project (OWASP). (2005). A guide to building secure webapplications and web services. OWASP. Retrieved from https://www.um.es/atica/documentos/OWASPGuide2.0.1.pdf

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CryptologyCourse Code: DLMCSEAITSC02



Study LevelMA

Admission RequirementsDLMCSEAITSC01; DLMCSITSDP01 or DLMCSITSDS01


Credit Points5



Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h



Type of Exam Fachpräsentation

Course DescriptionThe focus of this course is to provide a thorough introduction to cryptology and its main sub-disciplines cryptography and cryptanalysis. Particular emphasis is put on the use of cryptology tosupport the security of IT systems. In the first part of the courses, students gain a solidunderstanding of the basic concepts of cryptology, in particular symmetric and asymmetriccryptosystems, authentication, and common approaches to break these cryptosystems usingcryptanalysis. Based on this foundational understanding, the course goes on to cover the practicaluse of cryptology, starting with an introduction to the standard protocols and techniques used toensure the security of communication via the internet. Next, practical aspects of applyingcryptographic techniques and algorithms are covered, such as their long-term security. Finally,some application examples show how the concepts of cryptology are commonly used and can beused to solve challenges such as online banking.

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▪ discuss the main cryptographic systems and algorithms and their relevance in IT today.▪ discuss the security of internet-based applications.▪ evaluate different cryptographic systems and algorithms to select an appropriate solution

for real-world problems in IT.▪ apply standard cryptographic systems and algorithms to solve real-world problems in IT.▪ appraise existing cryptographic solutions to real-world problems and identify major

weaknesses where relevant.

Contents1. Basic concepts of cryptology

1.1 Introduction and terminology1.2 IT security, threats and common attacks1.3 Historical overview1.4 Kerckhoffs's principle

2. Symmetric cryptosystems2.1 Substitution and transposition2.2 Stream and block ciphers2.3 Digital encryption standard (DES)2.4 Advanced encryption standard (AES)

3. Asymmetric cryptosystems3.1 The RSA algorithm3.2 Elliptic curves3.3 Cryptographic hash functions3.4 Signatures and MACs3.5 Key exchange and public key infrastructures

4. Authentication4.1 Passwords4.2 Challenge-response and zero-knowledge4.3 Biometrics-based authentication4.4 Authentication in distributed systems4.5 Smartcards4.6 Identity and anonymity

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5. Cryptanalysis – how to break encryption5.1 Frequency analysis5.2 Brute-force attacks5.3 Rainbow tables5.4 Known/chosen plaintext5.5 Side-channel attacks

6. Cryptology and the internet6.1 Basic setup of the Internet and its protocols6.2 IPSec6.3 Transport Layer Security6.4 Secure E-Mail (TLS, S/MIME and PGP)6.5 Secure DNS

7. Practical aspects of cryptology7.1 Random number generation7.2 Long-term security (key lengths, perfect forward security, quantum computing)7.3 Incorporating cryptography into application development7.4 Legal and regulatory aspects

8. Applications8.1 Online banking8.2 Blockchain8.3 Voting8.4 Steganography and watermarks8.5 The Tor Project


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Literature

Further Reading▪ Beutelspacher, A. (1994). Cryptology. Washington, DC: Mathematical Association of America.▪ Ferguson, N., Schneier, B., & Kohno, T. (2010). Cryptography engineering. Design principles and

practical applications. Indianapolis, IN: Wiley.▪ Menezes, A. J., van Oorschot, P. C., & Vanstone, S. A. (2018). Handbook of applied cryptography.

Boca Raton, FL: CRC Press.▪ Paar, C., & Pelzl, J. (2011). Understanding cryptography: A textbook for students and

practitioners. Berlin, Heidelberg: Springer.▪ Singh, S. (2002). The code book: The secret history of codes and code-breaking. New York, NY:

Harper Collins.

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Blockchain and Quantum ComputingModule Code: DLMCSEBCQC



Study LevelMA

Credit Points10






Self Study220 h

Tutorial40 h

Self Test40 h

Hours Total300 h



▪ Blockchain (DLMCSEBCQC01)▪ Quantum Computing (DLMCSEBCQC02)

Module Exam Type

Module Exam Split ExamBlockchainWritten Assessment: Written Assignment ( 50)

Quantum ComputingOral Assignment ( 50)


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Module Contents

Blockchain▪ Basic concepts of blockchain and related technologies▪ Applications of blockchain and DLT▪ Security▪ Development of blockchain and DLT applications▪ Social and legal aspects

Quantum Computing▪ Physics of quantum computing▪ Quantum computing models▪ Quantum algorithms▪ Quantum computing with the IBM framework Qiskit▪ Applications, potential for and challenges of quantum computing

Learning OutcomesBlockchainOn successful completion, students will be able to▪ outline the functions provided by and the technology used in blockchains.▪ explain important applications of block chains, in particular BitCoin.▪ demonstrate the technical architecture of blockchain applications.▪ appraise the benefits and challenges of suggested blockchain applications.▪ discuss the social and legal aspects of blockchain technology.

Quantum ComputingOn successful completion, students will be able to▪ outline the basic concepts of quantum mechanics as they relate to quantum computing.▪ describe the computation models used in quantum computing.▪ demonstrate the role of quantum computing for cryptography and other application areas.▪ compare the theoretical and practical potential of quantum computing to classical

computing.▪ apply the concepts of quantum computing to develop simple programs within the Qiskit

framework.



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BlockchainCourse Code: DLMCSEBCQC01



Study LevelMA



Credit Points5



Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h



Type of Exam Written Assessment: Written Assignment

Course DescriptionStarted by the cryptocurrency BitCoin, blockchain and related topics such as distributed ledgertechnologies and smart contracts have become increasingly important over the last few years andare claimed to be a major disruptive technologies. As BitCoin shows, systems that today need atrustworthy central coordinating body may become genuinely distributed systems without theneed for such a body in the future. While blockchain has the potential for completely new types ofapplications, these suggested applications do not always make use of the strengths of thetechnology; rather, they simply provide a different approach to solving problems that could besolved more easily and efficiently using standard technologies such as database systems.Furthermore, blockchain applications have led to new social challenges and legal questions, suchas the legal status of “smart contracts”. Different infrastructures such as Ethereum andHyperledger have been developed to form the basis for blockchain applications. The goal of thiscourse is to provide an understanding of the technical, as well as social and legal, aspects ofblockchain and related technologies.

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▪ outline the functions provided by and the technology used in blockchains.▪ explain important applications of block chains, in particular BitCoin.▪ demonstrate the technical architecture of blockchain applications.▪ appraise the benefits and challenges of suggested blockchain applications.▪ discuss the social and legal aspects of blockchain technology.

Contents1. Basic Concepts

1.1 The Functional View: Distributed Ledger Technologies1.2 The Technical View: Blockchain1.3 History of Blockchain and DLT1.4 Consense Mechanisms

2. BitCoin2.1 The BitCoin Payment System2.2 The Technology Behind BitCoin2.3 Security of BitCoin2.4 Scalability and Other Limitations of BitCoin2.5 BitCoin Derivatives and Alternatives

3. Smart Contracts and Decentralized Apps3.1 Smart Contracts3.2 Decentralized Apps (DApps)3.3 Ethereum3.4 Hyperledger3.5 Alternative Platforms for Smart Contracts and DApps

4. Security of Block Chain and DLT4.1 Cryptology Used4.2 Attacks on Blockchain and DLT4.3 Resolving Bugs and Security Holes4.4 Long-Term Security

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5. Block Chain and DLT Application Scenarios5.1 Benefits and Limits of Applying Blockchain and DLT5.2 Registers for Land and Other Property5.3 Applications in the Supply Chain5.4 Applications in Insurance5.5 Initial Coin Offerings for Sourcing Capital5.6 Examples of Further Applications

6. Development of Blockchain and DLT Applications6.1 Architecture of Blockchain and DLT Applications6.2 Platform Selection6.3 Design of Blockchain and DLT Applications

7. Blockchain and Society7.1 (Mis-)Trust in Institutions7.2 Blockchain and the Environment7.3 Cyber-Currencies in the Darknet7.4 ICO Fraud

8. Legal Aspects8.1 DLT and Smart Contracts as Legal Contracts8.2 Cryptocurrencies as Legal Currencies8.3 Regulation of ICOs8.4 Data Protection / Privacy in Blockchains


Literature

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Further Reading▪ De Filippi, P., & Wright, A. (2018). Blockchain and the law. The rule of code. Cambridge, MA:

Harvard University Press.▪ Meinel, C., Gayvoronskaya, T. & Schnjakin, M. (2018). Blockchain. Hype or innovation. Potsdam:

Universitätsverlag Potsdam.▪ Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system [white paper]. Retrieved

from https://bitcoin.org/bitcoin.pdf▪ Tapscott, D., & Tapscott, N. (2018). Blockchain revolution. How the technology behind bitcoin

is changing money, business, and the world. New York, NY: Portfolio/Penguin.▪ Xu, W., Weber, I., & Staples, M. (2019). Architecture for blockchain applications. Cham: Springer.

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Quantum ComputingCourse Code: DLMCSEBCQC02



Study LevelMA



Credit Points5



Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h




Course DescriptionQuantum computing is a completely new paradigm for the architecture of computers. It currentlyis in the early stage of development but has the potential to speed up certain kinds ofcomputations, not just by orders of magnitude but by moving them from exponential to lineargrowth. One of the issues that will be affected is the prime factorization of large numbers whichcurrently forms the basis for important cryptographic algorithms, in particular the RSA algorithmwhich would in that case would no longer be secure. This course gives an introduction to thephysics behind quantum computing and the computation models used. Students are familiarizedwith the most important algorithms for quantum computing and write a few programs forquantum computers. The application potential and challenges of quantum computing are alsodiscussed.

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▪ outline the basic concepts of quantum mechanics as they relate to quantum computing.▪ describe the computation models used in quantum computing.▪ demonstrate the role of quantum computing for cryptography and other application areas.▪ compare the theoretical and practical potential of quantum computing to classical

computing.▪ apply the concepts of quantum computing to develop simple programs within the Qiskit

framework.

Contents1. Basic concepts

1.1 Quantum physics as a basis for computing1.2 Types of quantum computers1.3 Qbits1.4 Linear algebra

2. The physics of quantum computers2.1 Basic concepts of quantum mechanics2.2 Spin and entanglement2.3 Architecture of quantum computers2.4 Noise and error correction2.5 Current state and outlook

3. Quantum computing models3.1 Quantum gates and circuits3.2 Single qubit quantum systems3.3 Multiple qubit quantum systems

4. Quantum algorithms4.1 Computability and complexity in quantum computing4.2 Quantum Fourier transform4.3 The Shor algorithm4.4 The Grover algorithm

5. Quantum computing with the IBM framework Qiskit5.1 Overview of Qiskit and the IBM Q Provider5.2 Quantum circuits in Qiskit5.3 First steps in programming with Qiskit

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6. Applications, potential and challenges of quantum computing6.1 Applications of quantum computing6.2 Quantum cryptography and post-quantum cryptography6.3 Quantum supremacy


Literature

Further Reading▪ Bernhardt, C. (2019).

Quantum computing for everyone. Cambridge, MA: MIT Press.

▪ Faro, I. (2017).A developer’s guide to using the

QuantumQISKit SDK

. Retrieved from https://developer.ibm.com/code/2017/05/17/developers-guide-to-quantum-qiskit-sdk/

▪ Rieffel, E. G. (2014).Quantum computing. A gentle introduction

. Cambridge, MA: MIT Press.▪ Susskind, L., & Friedman, A. (2015).

Quantum mechanics. The theoretical minimum. London: Penguin.

▪ Zygelman, B. (2018).A first introduction to quantum computing and information

. Cham: Springer.

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DLMCSEBCQC02

IT Governance and Service ManagementModule Code: DLMBITGSM



Study LevelMA

Credit Points10





Self Study180 h

Tutorial60 h

Self Test60 h

Hours Total300 h

Module CoordinatorDr. André Köhler


▪ IT Service Management (DLMBITGSM01)▪ IT Governance and Compliance (DLMBITGSM02)

Module Exam Type

Module ExamModulklausur

Split Exam


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Module Contents

IT Service Management▪ IT infrastructure library (ITIL)▪ ITIL service strategy▪ ITIL service design▪ ITIL service transition▪ ITIL service operation

IT Governance and Compliance▪ Establishing IT governance and compliance▪ COBIT framework▪ IT governance frameworks▪ Data protection and data security

Learning OutcomesIT Service ManagementOn successful completion, students will be able to▪ understand IT service management as the enabler of information technology strategies and

operations objectives.▪ define the touchpoints between IT service management and management information

systems.▪ differentiate between lightweight and heavyweight approaches to IT service management.▪ understand benchmarks and assessments to measure the capability of a service provider

and its IT service management competences.▪ apply IT services management tools and platforms proactively based on current information

technology research and advisory.

IT Governance and ComplianceOn successful completion, students will be able to▪ explain IT governance and compliance both as tools to achieve organizational goals and to

satisfy regulatory requirements.▪ know the different IT governance frameworks given, in particular the industry standard

model COBIT.▪ set out the processes and policies for administering and managing IT systems for ensuring

compliance with local and international regulatory requirements.▪ understand that ensuring compliance with the IT governance framework can be a daunting

task that requires constant collection, organization, monitoring, analysis and reporting onevent logs to detect and manage control-related activity.

▪ recognize the IT governance and compliance monitoring tools for ensuring that controls forinformation systems are effectively implemented, monitored, and maintained.

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IT Service ManagementCourse Code: DLMBITGSM01



Study LevelMA



Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h




Course DescriptionThis course focuses on the nature and practice of IT services that keep IT systems running. Itintroduces students to the knowledge and experience needed to provide IT as a service toorganizations, mainly based on the IT Infrastructure Library (ITIL) which is the industry standardfor this purpose.

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▪ understand IT service management as the enabler of information technology strategies andoperations objectives.

▪ define the touchpoints between IT service management and management informationsystems.

▪ differentiate between lightweight and heavyweight approaches to IT service management.▪ understand benchmarks and assessments to measure the capability of a service provider

and its IT service management competences.▪ apply IT services management tools and platforms proactively based on current information

technology research and advisory.

Contents1. Introduction to IT Service Management

1.1 IT Services, Business IT Services1.2 Service Level Agreement (SLA)1.3 IT Service Management1.4 Reference Models for IT Service Management

2. IT Infrastructure Library (ITIL)2.1 Purpose and content of the IT Infrastructure Library2.2 Service Live Cycle in ITIL2.3 Overview on Service Strategy and Operational Processes2.4 Continual Service Improvement

3. ITIL – Service Strategy3.1 Business Relationship Management3.2 Service Portfolio Management3.3 Financial Management for Services3.4 Demand Management

4. ITIL – Operational Processes: Service Design4.1 Service Level Management4.2 Service Catalogue Management4.3 Availability Management4.4 Service Continuity Management

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5. ITIL – Operational Processes: Service Transition5.1 Transition Planning and Support5.2 Change Management5.3 Service Asset and Configuration Management5.4 Release and Deployment Management

6. ITIL – Operational Processes: Service Operation6.1 Incident Management6.2 Problem Management6.3 Request Fulfilment6.4 Event Management


Literature

Further Reading▪ Orand, B. (2011). Foundations of IT service management with ITIL 2011: ITIL foundations course

in a book. Create Space Independent Publishing Platform.▪ Sturm, R. (2000). Foundations of service level management (1st ed.). Hoboken, NJ: Sams

Publishing.▪ van Bon, J. (2007). Foundations of ITIL V3. Reading: Van Haren Publishing.

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IT Governance and ComplianceCourse Code: DLMBITGSM02



Study LevelMA



Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h




Course DescriptionIT governance and compliance are key elements within corporate governance, since most modernbusinesses rely heavily on IT infrastructure for their success. These elements detail the requiredleadership and organizational structures for maintaining and extending information technology inorder to meet business strategies and objectives.

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▪ explain IT governance and compliance both as tools to achieve organizational goals and tosatisfy regulatory requirements.

▪ know the different IT governance frameworks given, in particular the industry standardmodel COBIT.

▪ set out the processes and policies for administering and managing IT systems for ensuringcompliance with local and international regulatory requirements.

▪ understand that ensuring compliance with the IT governance framework can be a dauntingtask that requires constant collection, organization, monitoring, analysis and reporting onevent logs to detect and manage control-related activity.

▪ recognize the IT governance and compliance monitoring tools for ensuring that controls forinformation systems are effectively implemented, monitored, and maintained.

Contents1. About IT Governance

1.1 Concept and Definitions1.2 The Value of IT in the Organization1.3 Current State and Perceptions1.4 Governance, Compliance and Risk Management in IT

2. Establishing IT Governance and Compliance2.1 Assessment2.2 IT Strategy2.3 Tactics2.4 Operations2.5 Compliance2.6 Performance

3. The COBIT Framework3.1 Overview of COBIT3.2 The COBIT Goals Cascade3.3 The COBIT Process Reference Model3.4 Deploying and Implementing COBIT

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4. IT Governance Frameworks4.1 Quality Management as a Foundation4.2 ISO 9000 Family4.3 Maturity Models4.4 Relationship to Service and Architecture Frameworks (ITIL, TOGAF)4.5 Relationship to IT Security Frameworks (ISO 27000 family)

5. Data Protection and IT Security5.1 Data Protection5.2 IT Security Management5.3 IT Security Threats and Attack Scenarios5.4 Countermeasures5.5 Cryptography


Literature

Further Reading▪ Calder, A., Watkins, S., & Page, K. (2012). IT governance: An international guide to data security

and ISO27001/ISO27002. Philadelphia, PA: Kogan Page Ltd.▪ DeLuccia, J. (2008). IT compliance and controls: Best practices for implementation. Hoboken,

NJ: John Wiley & Sons.▪ ISACA (2012). Cobit 5 [Computer Software].▪ Selig, G. (2008). Implementing IT governance: A practical guide to global best practices in IT

management. Reading: Van Haren Publishing.

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DLMBITGSM02

UI/UX ExpertModule Code: DLMAIEUIUX



Study LevelMA

Credit Points10





Self Study210 h

Tutorial60 h

Self Test30 h

Hours Total300 h

Module CoordinatorProf. Dr. Ulrich Kerzel


▪ User Interface and Experience (DLMAIEUIUX01)▪ Project: Human Computer Interaction (DLMAIEUIUX02)

Module Exam Type

Module Exam Split ExamUser Interface and ExperienceExam, 90

Project: Human Computer InteractionPortfolio


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Module Contents

User Interface and Experience▪ ROI of UX design▪ Role and mindset of UX design in IT projects▪ The UX design process▪ UX psychology: How the human mind works▪ User research▪ UX design basics

In this course the students will gain practical experience in user experience design. They willconduct user testing for a given user interface and work on developing improvements. The workprocess and the results will become part of a portfolio.

Project: Human Computer Interaction

Learning OutcomesUser Interface and ExperienceOn successful completion, students will be able to▪ Understand what design is about and the crucial aspects of good design▪ understand and define the role of the UI/UX designer within a project.▪ explain the UX design process and the user-centered mindset.▪ advocate the importance of UX design for IT projects.▪ describe the basic methods of user research, user testing, and user-centered design.

Project: Human Computer InteractionOn successful completion, students will be able to▪ evaluate the usability of a user interface.▪ conduct user testing.▪ understand the practical implications of putting users first.▪ make small changes in existing user interfaces and recognize the situations in which a user

experience designer should be consulted.



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User Interface and ExperienceCourse Code: DLMAIEUIUX01



Study LevelMA



Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h




Course DescriptionUX design is crucial to the development of new IT services and applications and enhances thequality of the outcome. Applying UX design techniques can significantly and positively change thesoftware development process, and good UX design is the result of effective teamwork. Within thiscourse the students will understand the mindset, basic techniques, and impact of UX design on ITprojects. They will learn how the UX design process works and the role of the UX designer within ITprojects. They will also gain skills in the type of collaboration that produces the best results. Usingtheir basic knowledge about good design, the students will know when it is appropriate that theymake small changes to UIs themselves and when it is time to consult a designer.

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▪ Understand what design is about and the crucial aspects of good design▪ understand and define the role of the UI/UX designer within a project.▪ explain the UX design process and the user-centered mindset.▪ advocate the importance of UX design for IT projects.▪ describe the basic methods of user research, user testing, and user-centered design.

Contents1. ROI of UX design

1.1 Efficacy1.2 Efficiency1.3 The impact of design on use errors

2. Role and Mindset of UX design in IT projects2.1 The role of UX design: the UX designer2.2 The UX mindset: putting the user first

3. The UX design Process3.1 In a waterfall process environment3.2 In an agile process environment

4. UX Psychology: How the Human Mind Works4.1 Perceptual psychology4.2 Information processing4.3 Decision-making4.4 Situation awareness4.5 Errors

5. User Research5.1 The benefit of user research5.2 Basic research techniques5.3 User testing

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6. UX design Basics6.1 Interaction design6.2 Information architecture6.3 Screen design6.4 Graphic design6.5 Rules of good design


Literature

Further Reading▪ Cooper, A.,Reimann, R., Cronin, D., & Noessel, C. (2014). About face: The essentials of

interaction design. New York, NY: Wiley.▪ Johnson, J. (2010). Designing with the mind in mind. Burlington, MA: Elsevier.▪ Preece, J., Sharp, H., & Rogers, Y. (2015). Interaction design: Beyond human-computer

interaction. New York, NY: Wiley.▪ Microsoft Windows Dev Center. (2018). Guidelines. [Web page]. Retrieved from https://

docs.microsoft.com/en-us/windows/desktop/uxguide/guidelines▪ Unger, R., & Chandler, C. (2012). A project guide to UX design. Berkeley, CA: New Riders.

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Project: Human Computer InteractionCourse Code: DLMAIEUIUX02

Course TypeProject


Study LevelMA

Admission RequirementsDLMAIEUIUX01


Credit Points5



Self Study120 h

Tutorial30 h

Hours Total150 h




Course DescriptionIn this course the students will gain practical experience in user experience design. They will setup and conduct a user testing for a given user interface and develop improvements. The workprocess and the results will become part of a portfolio.


▪ evaluate the usability of a user interface.▪ conduct user testing.▪ understand the practical implications of putting users first.▪ make small changes in existing user interfaces and recognize the situations in which a user

experience designer should be consulted.

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Contents▪ User experience design focusses on the needs of users. Within this portfolio project the

students put into practice basic techniques which lead to good user-centered design. Theylearn how to test the user experience and usability of an application by conducting usertests, and they also learn how to develop and test ideas for improvement. Students willfinish this course having gained practical experience working within the mindset of puttingusers first.


Literature

Further Reading▪ Barnum, C. (2010): Usability Testing Essentials: Ready, Set...Test!, Morgan Kaufmann,

Burlington, USA▪ Cooper, A., Reimann, R., Cronin, D., & Noessel, C. (2014). About face: The essentials of

interaction design. New York, NY: Wiley.▪ Johnson, J. (2010). Designing with the mind in mind. Burlington, MA: Elsevier.▪ Preece, J., Sharp, H., & Rogers, Y. (2015). Interaction design: Beyond human-computer

interaction. New York, NY: Wiley.▪ Microsoft Windows Dev Center. (2018). Guidelines. [Web page]. Retrieved from https://

docs.microsoft.com/en-us/windows/desktop/uxguide/guidelines▪ Unger, R., & Chandler, C. (2012). A project guide to UX design. Berkeley, CA: New Riders.

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DLMAIEUIUX02

Data EngineerModule Code: DLMDSEDE



Study LevelMA

Credit Points10





Self Study230 h

Tutorial50 h

Self Test20 h

Hours Total300 h



▪ Data Engineering (DLMDSEDE01)▪ Project: Data Engineering (DLMDSEDE02)

Module Exam Type

Module Exam Split ExamData EngineeringOral Assignment

Project: Data EngineeringPortfolio


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Module Contents

Data Engineering▪ Principles of data engineering▪ Paradigms for data processing at scale▪ Overview on data governance, security, and protection▪ Common cloud platforms▪ DataOps approach

Project: Data Engineering▪ Knowledge transfer and application to practical problems▪ Implementation of a data infrastructure building block

Learning OutcomesData EngineeringOn successful completion, students will be able to▪ understand the foundational concepts in data engineering.▪ categorize important data-processing classes.▪ summarize common approaches to data governance and security.▪ compare different common public cloud offerings.▪ recognize current approaches to data operations (DataOps).

Project: Data EngineeringOn successful completion, students will be able to▪ apply the principles of data engineering to a practical application.▪ analyze data engineering approaches with respect to a given project task.▪ reason about the benefits and drawbacks of solution alternatives for a given implementation

task.▪ make apposite architectural choices.▪ implement aspects of a modern data pipeline



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Data EngineeringCourse Code: DLMDSEDE01



Study LevelMA



Credit Points5



Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h




Course DescriptionThe focus of this first course in the Data Engineering elective module is to introduce students toimportant principles, concepts, methods and approaches in this subject domain. In order toachieve this goal, the course moves from an exposition of the foundational principles of dataengineering to a thorough treatment of the core data processing classes. Modern architecturalparadigms such as Microservices are explained, and important factors in data governance andprotection are addressed. Aspects of cloud computing are introduced via an overview of the mostcommon offerings on the market. Finally, a state-of-the-art agile perspective on the operation ofdata pipelines is given by an exposition to the emerging notion of DataOps.

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▪ understand the foundational concepts in data engineering.▪ categorize important data-processing classes.▪ summarize common approaches to data governance and security.▪ compare different common public cloud offerings.▪ recognize current approaches to data operations (DataOps).

Contents1. Foundations of Data Systems

1.1 Reliability1.2 Scalability1.3 Maintainability

2. Data Processing at Scale2.1 Batch Processing2.2 Stream and Complex Event Processing

3. Microservices3.1 Introduction to Microservices3.2 Implementing Microservices

4. Governance & Security4.1 Data Protection4.2 Data Security4.3 Data Governance

5. Common Cloud Platforms & Services5.1 Amazon AWS5.2 Google Cloud5.3 Microsoft Azure

6. Data Ops6.1 Defining Principles6.2 Containerization6.3 Building Data Pipelines

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Literature

Further Reading▪ Kleppmann, M. (2017). Designing data intensive applications: The big ideas behind reliable,

scalable, and maintainable systems. Sebastopol, CA: O‘Reilly.▪ Farcic, V. (2016). The DevOps 2.0 toolkit: Automating the continuous deployment pipeline with

containerized microservices. Scotts Valley, CA: CreateSpace Independent Publishing Platform.▪ White, T. (2015). Hadoop: The definitive guide: Storage and analysis at Internet scale.

Sebastopol, CA: O’Reilley.▪ Karau, H., Konwinski, A., Wendell, P., & Zaharia, M. (2015). Learning Spark: Lightning fast data

analysis. Sebastopol, CA: O’Reilley.▪ Narkhede, N., Shapira, G., & Palino, T. (2017). Kafka: The definitive guide: Real-time data and

stream processing at scale. Sebastopol, CA: O’Reilley.

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Project: Data EngineeringCourse Code: DLMDSEDE02

Course TypeProject


Study LevelMA

Admission RequirementsDLMDSEDE01


Credit Points5



Self Study120 h

Tutorial30 h

Hours Total150 h




Course DescriptionThe second course of the Data Engineering elective module builds upon theoretical andmethodological insights from the first course. It provides opportunities for students to put theirnewly-acquired knowledge into practical application by completing a data engineering project.Inorder to find an appropriate and viable approach, students will have to reason about and evaluatethe benefits and drawbacks of possible architectural choices. Once an informed decision has beenmet, the chosen approach is implemented as a running piece of data infrastructure.


▪ apply the principles of data engineering to a practical application.▪ analyze data engineering approaches with respect to a given project task.▪ reason about the benefits and drawbacks of solution alternatives for a given implementation

task.▪ make apposite architectural choices.▪ implement aspects of a modern data pipeline

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Contents▪ The second course of the Data Engineering elective revolves around the implementation of a

data engineering project chosen from a set of project suggestions. Students can alsocontribute their own project ideas.


Literature

Further Reading▪ Kleppmann, M. (2017). Designing data intensive applications: The big ideas behind reliable,

scalable, and maintainable systems. Sebastopol, CA: O‘Reilly.▪ Farcic, V. (2016). The DevOps 2.0 toolkit: Automating the continuous deployment pipeline with

containerized microservices. Scotts Valley, CA: CreateSpace Independent Publishing Platform.▪ White, T. (2015). Hadoop: The definitive guide: Storage and analysis at Internet scale.

Sebastopol, CA: O’Reilley.▪ Karau, H., Konwinski, A., Wendell, P., & Zaharia, M. (2015). Learning Spark: Lightning fast data

analysis. Sebastopol, CA: O’Reilley.▪ Narkhede, N., Shapira, G., & Palino, T. (2017). Kafka: The definitive guide: Real-time data and

stream processing at scale. Sebastopol, CA: O’Reilley.

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DLMDSEDE02

Business AnalystModule Code: DLMDSEBA



Study LevelMA

Credit Points10





Self Study230 h

Tutorial50 h

Self Test20 h

Hours Total300 h



▪ Business Intelligence I (DLMDSEBA01)▪ Project: Business Intelligence (DLMDSEBA02)

Module Exam Type

Module Exam Split ExamBusiness Intelligence IWritten Assessment: Case Study

Project: Business IntelligencePortfolio


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Module Contents

Business Intelligence I▪ Data acquisition and dissemination▪ Data warehouse and multidimensional modeling▪ Analytical systems

Implementation of a business intelligence use case.

Project: Business Intelligence

Learning OutcomesBusiness Intelligence IOn successful completion, students will be able to▪ understand the motivations and use cases for, as well as fundamentals of, business

intelligence.▪ explain relevant types of data.▪ know and disambiguate techniques and methods for modeling and dissemination of data.▪ expound upon the techniques and methods for the generation and storage of

information.select apposite business intelligence methods for given requirements.

Project: Business IntelligenceOn successful completion, students will be able to▪ transfer knowledge of business intelligence methodology to real-world use cases.▪ analyze the suitability of different approaches with respect to the project task.▪ critically reason about relevant design choices.▪ make apposite architectural choices.▪ formulate and implement a business intelligence use case.

Links to other Modules within the StudyProgramThis module is similar to other modules in thefields of Computer Science & SoftwareDevelopment and Data Science & ArtificialIntelligence.


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Business Intelligence ICourse Code: DLMDSEBA01

Course TypeCase Study


Study LevelMA



Credit Points5



Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h



Type of Exam Written Assessment: Case Study

Course DescriptionBusiness Intelligence is about the generation of information based on operational data. It is usedto enable goal-oriented management practices as well as the optimization of relevant businessactivities. This course introduces and discusses techniques, methods, and models for dataprovisioning and the generation, analysis, and dissemination of information.


▪ understand the motivations and use cases for, as well as fundamentals of, businessintelligence.

▪ explain relevant types of data.▪ know and disambiguate techniques and methods for modeling and dissemination of data.▪ expound upon the techniques and methods for the generation and storage of

information.select apposite business intelligence methods for given requirements.

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Contents1. Motivation and Introduction

1.1 Motivation and historical development of the field1.2 Business intelligence as a framework

2. Data Provisioning2.1 Operative and dispositive systems2.2 The data warehouse concept2.3 Architecture variants

3. Data Warehouse3.1 The ETL-Process3.2 DWH and Data-Mart concepts3.3 ODS and meta-data

4. Modeling Multidimensional Dataspaces4.1 Data modeling4.2 OLAP-Cubes4.3 Physical storage concepts4.4 Star-Schema and Snowflake-Schema4.5 Historization

5. Analytical Systems5.1 Freeform data analysis and OLAP5.2 Reporting systems5.3 Model-based analytical systems5.4 Concept-oriented systems

6. Distribution and Access6.1 Information distribution6.2 Information access

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Literature

Further Reading▪ Kimball, R. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling

(3rd ed.). Indianapolis, IN: Wiley.▪ Linstedt, D., & Olschimke, M. (2015). Building a scalable data warehouse with Data Vault 2.0.

Waltham, MA: Morgan Kaufmann.▪ Provost, F. (2013). Data science for business: What you need to know about data mining and

data-analytic thinking. Sebastopol, CA: O'Reilly.▪ Sherman, R. (2014). Business intelligence guidebook: From data integration to analytics.

Waltham, MA: Morgan Kaufmann.▪ Turban, E., Sharda, R., Delen, D., & King, D. (2010). Business intelligence. A managerial

approach (2nd ed.). Upper Saddle River, NJ: Prentice Hall.

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Project: Business IntelligenceCourse Code: DLMDSEBA02

Course TypeProject


Study LevelMA

Admission RequirementsDLMDSEBA01


Credit Points5



Self Study120 h

Tutorial30 h

Hours Total150 h




Course DescriptionIn this course the students will transfer knowledge of business intelligence approaches andmethods to the implementation of a real-world business analytical use case. To accomplish thisgoal, students must look closely at the given task and find an apposite approach by analyzing,evaluating, and comparing different solution strategies and their constituent parts. The foundsolution then has to be implemented in order to arrive at a running business analytical system.


▪ transfer knowledge of business intelligence methodology to real-world use cases.▪ analyze the suitability of different approaches with respect to the project task.▪ critically reason about relevant design choices.▪ make apposite architectural choices.▪ formulate and implement a business intelligence use case.

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Contents▪ This second course in the Business Analyst specialization aims at the practical

implementation of a business intelligence project. Students can choose from a list of projecttopics or contribute their own ideas.


Literature

Further Reading▪ Kimball, R. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling

(3rd ed.). Indianapolis, IN: Wiley.▪ Linstedt, D., & Olschimke, M. (2015). Building a scalable data warehouse with Data Vault 2.0.

Waltham, MA: Morgan Kaufmann.▪ Provost, F. (2013). Data science for business: What you need to know about data mining and

data-analytic thinking. Sebastopol, CA: O'Reilly.▪ Sherman, R. (2014). Business intelligence guidebook: From data integration to analytics.

Waltham, MA: Morgan Kaufmann.▪ Turban, E., Sharda, R., Delen, D., & King, D. (2010). Business intelligence. A managerial

approach (2nd ed.). Upper Saddle River, NJ: Prentice Hall.

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DLMDSEBA02

Technical Project LeadModule Code: DLMDSETPL



Study LevelMA

Credit Points5






Self Study210 h

Tutorial60 h

Self Test30 h

Hours Total300 h

Module CoordinatorProf. Dr. Damir Ismailovic / Dr. Thomas Winkle


▪ IT Project Management (DLMBITPAM01)▪ Project: Technical Project Planning (DLMDSETPL01)

Module Exam Type

Module Exam Split ExamIT Project ManagementExam

Project: Technical Project PlanningPortfolio


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Module Contents

IT Project Management▪ Organizing the work▪ Cost estimation and controlling▪ The human factor▪ Organizing small, medium, and large projects

In this course, students learn to apply the project management concepts they learned in previousmodules in a real-world project.

Project: Technical Project Planning

Learning OutcomesIT Project ManagementOn successful completion, students will be able to▪ critically reflect the status of knowledge on IT project management.▪ set up different IT project management formats (small, medium and large projects) and know

the methods for managing these different IT projects professionally.▪ develop an IT management proposal as the fundament of a professional IT project

management concept.▪ understand and integrate different IT management project plans (e.g., time plan, cost plan,

resources plan, risk plan) and use those plans in an integrative IT project planning andcontrolling scheme.

▪ organize and to lead an IT project team and its core and/or extended team members.

Project: Technical Project PlanningOn successful completion, students will be able to▪ apply the concepts of project management to real-world tasks and problems.▪ translate the learned theories into the practice of project management.▪ analyze a real-world problem and define and implement a project to resolve it.▪ appraise the results of a project performed and identify what worked well and what did not.▪ explain the work they perform, give its scientific background, and produce adequate

documentation.

Links to other Modules within the StudyProgramThis module is similar to other modules in thefields of Computer Science & SoftwareDevelopment and Data Science & ArtificialIntelligence.


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IT Project ManagementCourse Code: DLMBITPAM01



Study LevelMA



Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h



Type of Exam Exam

Course DescriptionThe purpose of this course is to introduce students to the concepts involved in IT projectmanagement. This is achieved through the development of an understanding of the fundamentaltenets of project management enhancing the students’ ability to apply their knowledge, skills andcompetencies in analyzing and solving IT project management problems. A special focus is put onthe specifics of IT project organization, cost management and the human factor within IT projects.

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▪ critically reflect the status of knowledge on IT project management.▪ set up different IT project management formats (small, medium and large projects) and

know the methods for managing these different IT projects professionally.▪ develop an IT management proposal as the fundament of a professional IT project

management concept.▪ understand and integrate different IT management project plans (e.g., time plan, cost plan,

resources plan, risk plan) and use those plans in an integrative IT project planning andcontrolling scheme.

▪ organize and to lead an IT project team and its core and/or extended team members.

Contents1. Introduction: Characteristics of IT Projects

1.1 Defining IT Projects1.2 Overview on Typical Roles and Phases of IT Projects1.3 Risks and Challenges of IT Projects1.4 Role of an IT Project Manager

2. Organizing the Work2.1 Project Breakdown Structure, Work Packages2.2 Prioritization2.3 Time Planning, Milestones, Gantt-Diagram2.4 Definition of Done

3. Cost Estimation and Controlling3.1 Challenges of Cost Estimation in IT Projects3.2 Estimation Techniques: 3-Point Estimation, Double Blind Expert Estimation, Function

Points3.3 Cost Controlling Using Earned Value Analysis3.4 Risk Management

4. The Human Factor4.1 Vision Keeping4.2 Stakeholder Management4.3 Conflict Management

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5. Organizing Small and Medium Projects5.1 Rational Unified Process (RUP)5.2 Agile Software Processes5.3 Scrum5.4 Plan-driven Project Management in Small Projects

6. Organizing Large Projects6.1 PMBOK Guide6.2 Prince26.3 Multi Project Management6.4 Agile Software Processes in Large Projects6.5 Selection of the Appropriate Project Management Method


Literature

Further Reading▪ Hinde, D. (2012). PRINCE2 Study Guide. West Sussex: John Wiley & Sons.▪ Kneuper, R. (2018). Software processes and lifecycle models. Cham: Springer Nature

Switzerland.▪ Phillips, J. (2010). IT project management: On track from start to finish (3rd ed.). New York, NY:

McGraw-Hill.▪ Project Management Institute. (2013). A guide to the project management body of knowledge:

PMBOK guide.▪ Schwaber, K. (2004). Agile project management with Scrum. Redmond, WA: Microsoft Press.

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Project: Technical Project PlanningCourse Code: DLMDSETPL01

Course TypeProject


Study LevelMA

Admission RequirementsDLMBITPAM01


Credit Points5



Self Study120 h

Tutorial30 h

Hours Total150 h




Course DescriptionThe focus of this course is to apply the project management knowledge gained previously in apractical portfolio project and reflect on the results. Students engage in this portfolio project anddocument the results, reflecting on the project management concepts they apply and theinfluence of these concepts on the success of the project.


▪ apply the concepts of project management to real-world tasks and problems.▪ translate the learned theories into the practice of project management.▪ analyze a real-world problem and define and implement a project to resolve it.▪ appraise the results of a project performed and identify what worked well and what did not.▪ explain the work they perform, give its scientific background, and produce adequate

documentation.

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Contents▪ In this course, students perform and document a portfolio project in which they apply the

project management topics covered in previous modules.


Literature

Further Reading▪ Hinde, D. (2012). PRINCE2 Study Guide. West Sussex: John Wiley & Sons.▪ Kneuper, R. (2018). Software processes and lifecycle models. Cham: Springer Nature

Switzerland.▪ Phillips, J. (2010). IT project management: On track from start to finish (3rd ed.). New York, NY:

McGraw-Hill.▪ Project Management Institute. (2013). A guide to the project management body of knowledge:

PMBOK guide.▪ Schwaber, K. (2004). Agile project management with Scrum. Redmond, WA: Microsoft Press.

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DLMDSETPL01

Machine Learning and Deep LearningModule Code: DLMCSEMLDL



Study LevelMA

Credit Points10





Self Study200 h

Tutorial50 h

Self Test50 h

Hours Total150 h



▪ Machine Learning (DLMDSML01)▪ Deep Learning (DLMDSDL01)

Module Exam Type

Module Exam Split ExamMachine LearningExam

Deep LearningOral Assignment


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Module Contents

Machine Learning▪ Supervised, unsupervised, and reinforcement learning approaches▪ Regression and classification learning problems▪ Estimation of functional dependencies via regression techniques▪ Data clustering▪ Support vector machines, large margin classification▪ Decision tree learning

Deep Learning▪ Introduction to neural networks and deep earning▪ Network architectures▪ Neural network training▪ Alternative training methods▪ Further network architectures

Learning OutcomesMachine LearningOn successful completion, students will be able to▪ know different machine learning model classes.▪ comprehend the difference between supervised, unsupervised, and reinforcement learning

methods.▪ understand common machine learning models.▪ analyze trade-offs in the application of different models.▪ appropriately choose machine learning models according to a given task.

Deep LearningOn successful completion, students will be able to▪ comprehend the fundamental building blocks of neural networks.▪ understand concepts in deep learning.▪ analyze the relevant deep learning architecture in a wide range of application scenarios.▪ create deep learning models.▪ utilize alternative methods to train deep learning models.



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Machine LearningCourse Code: DLMDSML01



Study LevelMA

Admission RequirementsDLMDSAM01, DLMDSPWP01


Credit Points5



Self Study90 h

Tutorial30 h

Self Test30 h

Hours Total150 h



Type of Exam Exam

Course DescriptionMachine learning is a field of scientific study concerned with algorithmic techniques that enablemachines to learn performance on a given task via the discovery of patterns or regularities inexemplary data. Consequently, its methods commonly draw upon a statistical basis in conjunctionwith the computational capabilities of modern computing hardware. This course aims to acquaintthe student with the main branches of machine learning and provide a thorough introduction tothe most widely used approaches and methods in this field.


▪ know different machine learning model classes.▪ comprehend the difference between supervised, unsupervised, and reinforcement learning

methods.▪ understand common machine learning models.▪ analyze trade-offs in the application of different models.▪ appropriately choose machine learning models according to a given task.

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Contents1. Introduction to Machine Learning

1.1 Regression & Classification1.2 Supervised & Unsupervised Learning1.3 Reinforcement Learning

2. Clustering2.1 Introduction to clustering2.2 K-Means2.3 Expectation Maximization2.4 DBScan2.5 Hierarchical Clustering

3. Regression3.1 Linear & Non-linear Regression3.2 Logistic Regression3.3 Quantile Regression3.4 Multivariate Regression3.5 Lasso & Ridge Regression

4. Support Vector Machines4.1 Introduction to Support Vector Machines4.2 SVM for Classification4.3 SVM for Regression

5. Decision Trees5.1 Introduction to Decision Trees5.2 Decision Trees for Classification5.3 Decision Trees for Regression

6. Genetic Algorithms6.1 Introduction to Genetic Algorithms6.2 Applications of Genetic Algorithms

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Literature

Further Reading▪ Bishop, C. M. (2011). Pattern recognition and machine learning. New York, NY: Springer.▪ Efron, B., & Hastie, T. (2016). Computer age statistical inference. Cambridge: Cambridge

University Press.▪ Muller, A. C., & Guido, S. (2016). Introduction to machine learning with Python. Sebastopol, CA:

O’Reilly.▪ VanderPlas, J. (2017). Python data science handbook. Sebastopol, CA: O’Reilly Publishing.

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Deep LearningCourse Code: DLMDSDL01



Study LevelMA

Admission RequirementsDLMDSAM01, DLMDSPWP01, DLMDSML01


Credit Points5



Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h




Course DescriptionNeural networks and deep learning approaches have revolutionized the fields of data science andartificial intelligence in recent years, and applications built on these techniques have reached orsurpassed human performance in many specialized applications.After a short review of the originsof neural networks and deep learning, this course will cover the most common neural networkarchitectures and discuss in detail how neural networks are trained using dedicated data samples,avoiding common pitfalls such as overtraining.The course includes a detailed overview ofalternative methods to train neural networks and further network architectures which are relevantin a wide range of specialized application scenarios.

DLMDSDL01162

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▪ comprehend the fundamental building blocks of neural networks.▪ understand concepts in deep learning.▪ analyze the relevant deep learning architecture in a wide range of application scenarios.▪ create deep learning models.▪ utilize alternative methods to train deep learning models.

Contents1. Introduction to Neural Network and Deep Learning

1.1 The Biological Brain1.2 Perceptron and Multi-Layer Perceptrons

2. Network Architectures2.1 Feed-Forward Networks2.2 Convolutional Networks2.3 Recurrent Networks, Memory Cells and LSTMs

3. Neural Network Training3.1 Weight Initialization and Transfer Function3.2 Backpropagation and Gradient Descent3.3 Regularization and Overtraining

4. Alternative Training Methods4.1 Attention4.2 Feedback Alignment4.3 Synthetic Gradients4.4 Decoupled Network Interfaces

5. Further Network Architectures5.1 Generative Adversarial Networks5.2 Autoencoders5.3 Restricted Boltzmann Machines5.4 Capsule Networks5.5 Spiking Networks

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Literature

Further Reading▪ Chollet, F. (2017). Deep learning with Python. Shelter Island, NY: Manning.▪ Efron, B., & Hastie, T. (2016). Computer age statistical inference. Cambridge: Cambridge

University Press.▪ Geron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow. Boston, MA:

O’Reilly Publishing.▪ Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Boston, MA: MIT Press.▪ Russel, S., & Norvig, P. (2010). Artificial intelligence – A modern approach (3rd ed.). Essex:

Pearson.

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Use Case Identification and Evaluation for AnalyticalApplications

Module Code: DLMCSEUCIAA


Admission Requirements▪ none▪ DLMDSUCE01

Study LevelMA

Credit Points10





Self Study230 h

Tutorial50 h

Self Test20 h

Hours Total300 h

Module CoordinatorN.N


▪ Use Case and Evaluation (DLMDSUCE01)▪ Project: Data Science Use Case (DLMDSPDSUC01)

Module Exam Type

Module Exam Split ExamUse Case and EvaluationFachpräsentation

Project: Data Science Use CasePortfolio


165DLMCSEUCIAA

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Module Contents

Use Case and Evaluation▪ Use case evaluation▪ Model-centric evaluation▪ Business-centric evaluation▪ Monitoring▪ Avoiding common fallacies▪ Change management

Project: Data Science Use Case

Learning OutcomesUse Case and EvaluationOn successful completion, students will be able to▪ analyze use cases and their requirements regarding the project objectives.▪ apply common metrics to evaluate predictions.▪ evaluate key performance indicators to asses projects from a business perspective.▪ create monitoring tools that can be used to constantly evaluate the status quo of a project.▪ understand common fallacies and how to avoid them.

Project: Data Science Use CaseOn successful completion, students will be able to▪ apply the concepts covered in the preceding data science courses to build a running

analytical model or system.▪ explain the design choices made in the selection of the employed model and its

implementation.▪ transfer acquired theoretical knowledge to real case studies.▪ translate the learned theories into the practice of data science system building.▪ critically evaluate the resulting model or system’s performance


Links to other Study Programs of IUBHAll Master Programmes in the IT & Technologyfields.

166 DLMCSEUCIAA

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Use Case and EvaluationCourse Code: DLMDSUCE01



Study LevelMA



Credit Points5



Self Study110 h

Tutorial20 h

Self Test20 h

Hours Total150 h



Type of Exam Fachpräsentation

Course DescriptionThe evaluation and definition of use cases is the fundamental groundwork from which theprojects can be defined. This does not only include the scope and technical requirements of aproject but also how value can be derived from the project. A crucial aspect is the definition ofwhat makes a project successful, both in terms of a technical evaluation as well as a businesscentric perspective and how the status quo can be monitored effectively during the progress of aproject. The course also discusses how to avoid common fallacies and understand theimplications of introducing data-driven decisions into traditional management structures.


▪ analyze use cases and their requirements regarding the project objectives.▪ apply common metrics to evaluate predictions.▪ evaluate key performance indicators to asses projects from a business perspective.▪ create monitoring tools that can be used to constantly evaluate the status quo of a project.▪ understand common fallacies and how to avoid them.

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Contents1. Use Case Evaluation

1.1 Identification of Use Cases1.2 Specifying Use Case Requirements1.3 Data Sources and Data Handling Classification

2. Model-centric Evaluation2.1 Common Metrics for Regression and Classification2.2 Visual Aides

3. Business-centric Evaluation3.1 Cost Function and Optimal Point Estimators3.2 Evaluation Using KPIs3.3 A/B Test

4. Monitoring4.1 Visual Monitoring Using Dashboards4.2 Automated Reporting and Alerting

5. Avoiding Common Fallacies5.1 Cognitive Biases5.2 Statistical Effects5.3 Change Management: Transformation to a Data-driven Company


Literature

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Further Reading▪ Few, S. (2013). Information dashboard design: Displaying data for at-a-glance monitoring (2nd

ed.). Burlingame, CA: Analytics Press.▪ Gilliland, M., Tashman, L., & Sglavo, U. (2016). Business forecasting: Practical problems and

solutions. Hoboken, NJ: John Wiley & Sons.▪ Hyndman, R. (2018). Forecasting: Principles and practices (2nd ed.). OTexts.▪ Kahneman, D. (2012). Thinking, fast and slow. London: Penguin.▪ Parmenter, D. (2015). Key Performance Indicators (KPI): Developing, implementing, and using

winning KPIs (3rd ed.). Hoboken, NJ: John Wiley & Sons.

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Project: Data Science Use CaseCourse Code: DLMDSPDSUC01

Course TypeProject


Study LevelMA

Admission RequirementsDLMDSUCE01


Credit Points5



Self Study120 h

Tutorial30 h

Hours Total150 h




Course DescriptionIn this course, students choose a project task in accord with their tutor from a variety of options.The goal is to prototypically implement a data science model or system in a suitable developmentenvironment. The choice of approach, the system or software implemented, and the resultingperformance on the task are to be reasoned about, explained, and documented in a projectreport. To this end, students make practical use of the methodological knowledge acquired inprevious courses by applying them to relevant real-world problems.

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▪ apply the concepts covered in the preceding data science courses to build a runninganalytical model or system.

▪ explain the design choices made in the selection of the employed model and itsimplementation.

▪ transfer acquired theoretical knowledge to real case studies.▪ translate the learned theories into the practice of data science system building.▪ critically evaluate the resulting model or system’s performance

Contents▪ In this project course the students work on a practical implementation of a data science use

case of their choosing. All relevant artifacts like use case evaluation, chosen implementationmethod, code, and outcomes are to be documented in the form of a written project report.


Literature

Further Reading▪ Few, S. (2013). Information dashboard design: Displaying data for at-a-glance monitoring (2nd

ed.). Burlingame, CA: Analytics Press.▪ Gilliland, M., Tashman, L., & Sglavo, U. (2016). Business forecasting: Practical problems and

solutions. Hoboken, NJ: John Wiley & Sons.▪ Hyndman, R. (2018). Forecasting: Principles and practices (2nd ed.). OTexts.

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DLMDSPDSUC01

4. Semester

Master ThesisModule Code: MMTHE



Study LevelMA

Credit Points30





Self Study900 h

Hours Total900 h

Module CoordinatorProf. Dr. Holger Sommerfeldt


▪ Master Thesis (MMTHE01)▪ Colloquium (MMTHE02)

Module Exam Type

Module Exam Split ExamMaster ThesisMasterarbeit ( 90)

Colloquium( 10)


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Module Contents

Master Thesis▪ Master's thesis

Colloquium▪ Colloquium on the Master's thesis

Learning OutcomesMaster ThesisOn successful completion, students will be able to▪ work on a problem from their major field of study by applying the specialist and

methodological skills they have acquired during their studies.▪ analyse selected tasks with scientific methods, critically evaluate them and develop

appropriate solutions under the guidance of an academic supervisor.▪ record and analyse existing (research) literature appropriate to the topic of the Master's

thesis.▪ prepare a detailed written elaboration in compliance with scientific methods.

ColloquiumOn successful completion, students will be able to▪ present a problem from their field of study under consideration of academic presentation

and communication techniques.▪ reflect on the scientific and methodological approach chosen in the Master's thesis.▪ actively answer subject-related questions from subject experts (experts of the Master's

thesis).

Links to other Modules within the StudyProgramThis module is similar to other modules in thefield(s) of Methods.

Links to other Study Programs of IUBHAll Master Programmes in the Business &Management field(s).

178 MMTHE

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Master ThesisCourse Code: MMTHE01

Course TypeThesis-Kurs


Study LevelMA


Field of StudiesMethoden

Credit Points27



Self Study810 h

Hours Total810 h



Type of Exam Masterarbeit

Course DescriptionThe aim and purpose of the Master's thesis is to successfully apply the subject-specific andmethodological competencies acquired during the course of study in the form of an academicdissertation with a thematic reference to the major field of study. The content of the Master'sthesis can be a practical-empirical or theoretical-scientific problem. Students should prove thatthey can independently analyse a selected problem with scientific methods, critically evaluate itand work out proposed solutions under the subject-methodological guidance of an academicsupervisor. The topic to be chosen by the student from the respective field of study should notonly prove the acquired scientific competences, but should also deepen and round off theacademic knowledge of the student in order to optimally align his professional abilities and skillswith the needs of the future field of activity.

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▪ work on a problem from their major field of study by applying the specialist andmethodological skills they have acquired during their studies.

▪ analyse selected tasks with scientific methods, critically evaluate them and developappropriate solutions under the guidance of an academic supervisor.

▪ record and analyse existing (research) literature appropriate to the topic of the Master'sthesis.

▪ prepare a detailed written elaboration in compliance with scientific methods.

Contents▪ Within the framework of the Master's thesis, the problem as well as the scientific research

goal must be clearly emphasized. The work must reflect the current state of knowledge of thetopic to be examined by means of an appropriate literature analysis. The student must provehis ability to use the acquired knowledge theoretically and/or empirically in the form of anindependent and problem-solution-oriented application.

Instructional Methods☐ Course Book☐ Interactive Book☐ Vodcast/Shortcast☐ Podcast☐ Exam Template☐ Review Book☐ Sprint☐ Guidelines☐ Live Tutorials☐ MyCampus Forum☐ Piazza

Literature

Further Reading▪ Hunziker, A. W. (2010): Fun at scientific work. This is how you write a good semester, bachelor

or master thesis. 4th edition, SKV, Zurich.▪ Wehrlin, U. (2010): Scientific work and writing. Guide to the preparation of Bachelor's theses,

Master's theses and dissertations - from research to book publication. AVM, Munich.▪ Selection of literature according to topic

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ColloquiumCourse Code: MMTHE02

Course TypeThesis Defense


Study LevelMA



Credit Points3



Self Study90 h

Hours Total90 h



Type of Exam

Course DescriptionThe colloquium will take place after submission of the Master's thesis. This is done at theinvitation of the experts. During the colloquium, the students must prove that they have fullyindependently produced the content and results of the written work. The content of thecolloquium is a presentation of the most important work contents and research results by thestudent, and the answering of questions by the experts.


▪ present a problem from their field of study under consideration of academic presentationand communication techniques.

▪ reflect on the scientific and methodological approach chosen in the Master's thesis.▪ actively answer subject-related questions from subject experts (experts of the Master's

thesis).

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Contents▪ The colloquium includes a presentation of the most important results of the Master's thesis,

followed by the student answering the reviewers' technical questions.

Instructional Methods☐ Course Book☐ Interactive Book☐ Vodcast/Shortcast☐ Podcast☐ Exam Template☐ Review Book☐ Sprint☐ Guidelines☐ Live Tutorials☐ MyCampus Forum☐ Piazza

Literature

Further Reading▪ Renz, K.-C. (2016): The 1 x 1 of the presentation. For school, study and work. 2nd edition,

Springer Gabler, Wiesbaden.

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