After completing the course, students will have mastered the most important principles of object-oriented programming on a small scale and have a basic understanding of the structure and functioning of computers.

Technical and methodological competence:
You will acquire the formal competence to understand the principles, methods, concepts and notations of programming on a small scale, to classify them in different contexts and to use them in object-oriented programs. This also includes identifying the algorithmic core of a simple problem and designing an imperative algorithm.
They acquire basic analysis skills that enable them to implement simple object-oriented models in UML notation in the Java programming language. This competence also includes the ability to familiarize themselves independently with applications (such as development environments, learning platforms).
You have the implementation skills to develop and analyze object-oriented programs in Java.

• H. Balzert, Java: Der Einstieg in die Programmierung, 4. Auflage, Springer Campus, 2013
• H. Balzert, Java: Objektorientiert programmieren, 3. Auflage, Springer Campus, 2017
• H. P. Gumm, M. Sommer, Grundlagen der Informatik: Programmierung, Algorithmen und Datenstrukturen, Oldenbourg, 2016
• S. Goll, C. Heinisch, Java als erste Programmiersprache, 8. Auflage, Springer Vieweg, 2016
• D. Ratz, J. Scheffler, D. Seese, J. Wiesenberger, Grundkurs Programmieren in Java, 7. Auflage, Hanser, 2014
• C. Ullenboom, Java ist auch eine Insel, 12. Auflage, Galileo Press, 2016 (siehe auch http://openbook.galileocomputing.de/javainsel/)

Projektwoche

Das Modul beinhaltet eine Projektwoche (I9PB-41012, 2 SWS). Die Klausurarbeit und die Projektwoche können unabhänig voneinander abgelegt werden. Für das Bestehen des Moduls ist neben einer Klausur die erfolgreiche Teilnahme an der Projektwoche erforderlich. Die Note des Moduls wird ausschließlich über die Klausurarbeit definiert. Die Projektwoche wird als 5-Tägige Blockveranstaltung im Anschluss an die Vorlesung angeboten.

Technical and methodological competence:

• Students master basic mathematical concepts of computer science and their methods such as set theory, relations, propositional logic, complex numbers as well as groups and solids.
• Students who have completed the module have mastered basic and advanced concepts and methods from linear algebra and are able to apply these methods with reference to their practical applications to solve typical tasks in computer science.
• The graduates demonstrate a confident handling of the concepts and methods of vector and matrix calculus and their geometric interpretation, setting up and solving linear systems of equations as well as dealing with straight lines and planes.

Interdisciplinary methodological skills and self-competence:

• Graduates of the module are able to solve computer science problems by setting up and calculating the corresponding mathematical models (for example by setting up and solving linear systems of equations). They demonstrate confidence in the appropriate selection of problem-specific solution methods and their application.
The students are able to recognize the mathematical structures they have learned in other areas of computer science and to transfer the methods they have learned to these areas.

Social skills:

• The participants understand the relevance of the content taught to their field of study and are able to communicate this relevance adequately.

The event includes the following topics:

• Basics of mathematics for computer scientists: Introduction to set theory, cardinality of sets, relations, basics of propositional logic, complex numbers, groups and solids.
• Vectors and vector calculus: notation and interpretation, operations on vectors and their properties (addition, scalar multiplication, scalar product, cross product), vector spaces, length of vectors, collinearity, linear dependence and independence, concepts of dimension and basis, angles between vectors.
• Lines and planes: Representation in linear algebra, applications, positional relationships between points / straight line / planes
• Matrices: Notation and interpretation, operations on matrices and their properties (transposing matrices, addition, scalar multiplication, matrix multiplication), Gaussian algorithm, determinants, inverse matrices and their calculation
• Linear systems of equations: motivation and applications, matrix-vector form of linear systems of equations, Gaussian algorithm for solving linear systems of equations, homogeneous and inhomogeneous linear systems of equations and their relationships, rank of a matrix and relation to the solution set of linear systems of equations
• Eigenvalues and basic transformations

Students learn about the basic structure of a computer, from simple digital circuits to a typical microprocessor and computer architectures to basic concepts of an operating system.

Technical and methodological competence

• Computer-oriented representation of information (numbers and characters)
• Describing gates and their function, designing simple switching networks, specifying the function of a switching network as a Boolean expression and as a truth table
• Understanding the structure and use of memory elements (selected latches and flip-flops)
• Sketch the structure and basic understanding of how microprocessors and computer architectures work
• Understanding simple machine programs
• Sketch and evaluate simple implementations of the three central tasks of an operating system (process, memory and file management)
• Practical application of the Linux operating system

Social skills

• Solving programming tasks in groups of two
• Presenting the results to the supervisor

Technical and methodological competence:

• Be able to name basic terms and properties of formal languages, grammars and the corresponding automata
.
• Create grammars and automata for formal languages and understand how they work.
• Be able to convert the representation of languages between grammars, automata and regular expressions.
Be able to independently assess problems as formal languages and classify them with regard to the language types in the Chomsky hierarchy.

Interdisciplinary methodological competence:

• Be able to independently assess and classify problems in terms of their complexity
.

Ability to extract information from data using statistical methods, especially regression methods.

Technical and methodological competence:

• Acquisition of methodological knowledge of explorative and inductive statistics
• Formulating statistical models, especially regression models
• Selection and implementation of parameter estimation, model selection, model testing with subsequent interpretation of results
• Calculating forecasts and forecast intervals
• Conducting and analyzing real experiments and computer simulations based on statistical experimental design
• Model-based optimization of technical and logistical processes
• Independent analysis of data sets with statistical software (R, JMP,...) and documentation in report form

Interdisciplinary methodological competence:

• Supporting decision-making processes through data analysis
• Creating forecasts with uncertainty estimation based on data sets
• Applying statistical methods in connection with the evaluation of databases

• Definition of the classical linear model
• Model parameters, ML and KQ estimation
• Hypothesis testing in the context of regression models
• Residual analysis
• Model selection and variable selection
• Model interpretation, forecasting and forecasting intervals
• Basics of statistical experimental design (experimental design, experimental range, coding, randomization, repetitions, block formation)
• Screening and optimization plans, space-filling plans
• Insight into various statistical models (analysis of variance, generalized linear models, Gaussian process models, )

In the course "Selected Aspects of Practical Computer Science", content on a special topic of practical computer science is presented.

This course offers the opportunity to offer a course that is not offered on a regular annual basis, and lecturers from Germany and abroad and cooperation partners can be approached to present interesting aspects.

The topics offered specifically expand the range of courses in the field of practical computer science.

Technical and methodological skills

• The students know the basics of the topic
• The students know the requirements, principles, architectures, methods, procedures and tools for the topic
• The students can work independently on tasks (case studies, project tasks, development tasks)
.

Self-competence

• Students develop their results independently or in teams and present them
.

Social competence:

• Practical work is carried out in teams
.

In this course, a lecturer will specifically present 'Selected Aspects of Practical Computer Science'

This course is offered in coordination with the Dean of Studies, taking capacity aspects into account.

A module description - in accordance with the specifications in the module handbook - is created in advance for the specific course. The head of degree program uses this to check the suitability of the course to complement the curriculum. The module description is made available to the students from the beginning of the course.

Technical and methodological competence:
Students acquire comprehensive, theoretical and practical knowledge about the use of various business intelligence solutions and identify the challenges and opportunities associated with the planning and implementation of a business intelligence solution in addition to basic knowledge in the areas of data extraction, data modeling and data presentation. Students analyze various methods for designing business intelligence solutions (top-down approach, bottom-up, etc.). They also determine different analysis methods that can be used and assigned depending on requirements.

Interdisciplinary methodological expertise:
The use of top-down and bottom-up methods is also transferable to other IT application areas and helps students, for example, in the design and implementation of operational software solutions. Furthermore, the knowledge acquired can also be used in the area of project management.

Self-competence:
The students' individual willingness to perform is encouraged within the framework of the exercises on the system through targeted incentives - comparable to a "competitive situation" in the sense of measuring themselves against other groups.

Social skills:
Students solve problems independently on the basis of various case studies with the aid of a business intelligence solution. Students test their knowledge practically in the form of exercises that are solved with the help of standard application software, thereby differentiating their specialist knowledge. The exercises and case studies are designed as group work and thus promote communication skills. In addition, the solutions are presented to the group, thus improving presentation skills.

Professional field orientation:
The use of current software solutions in this course qualifies students to efficiently use or set up a business intelligence solution in their day-to-day work. The use of such a solution is possible in all functional areas of the company. The knowledge acquired is thus also applicable to the current strong market demand for graduates with BI skills - in the field of IT consulting.

Seminar-type course:

• Basics of Business Intelligence
• Applications of business intelligence
• Data provision and data modeling

Exercise:

• Reporting case with pivot tables in Microsoft Excel
• Reporting case with SAP Analysis for Office
• Queries with SAP Query Designer
• Modeling with SAP BW
• ETL process with SAP BW

With regard to technical and methodological competence, students can recognize, analyse and classify the characteristics of collaborative business, through which cross-company and cross-company integration is realized throughout the entire product life cycle management up to the integration of the consumer. They are able to identify, select and evaluate organizational forms, standards and tools. Students will be able to differentiate between process-oriented and communication-oriented collaboration, classify different collaboration scenarios and analyze, classify and evaluate corresponding case studies as reference models.

They are able to examine, include and weigh up technical, organizational and Business Studies aspects as part of the analysis and discussion of case studies and thus deal with professional field-specific problems that often arise in the context of change projects

Procedures and methods are taught that can be used to design cross-company processes from the development to the delivery of a product. In such processes, it is also possible for process participants to gain access to and use the resources of partner companies. This opens up new potential for value creation, creating synergy effects and avoiding data redundancies and the multiple execution of activities and their consequences. New concepts of interface realization, such as service-oriented architectures (SOA), are just as important as identity management for the proper administration of user data. The developments of the 2nd Interation (business communities and social software) also represent a success factor and should therefore be addressed.

The following will be covered:

• Historical development of C-Business
• C-Business Process Management
• Organizational forms of C-business (portals/marketplaces/knowledge management systems)
• Standards and tools, in particular interfaces (EAI/EDI/cXML/ECA/SOA)
• Business communities and social software
• C-Business scenarios (case studies)

Technical and methodological competence

• Students are able to use an ERP system to monitor and control a complex business process
.
• The students can describe the use of an ERP system with regard to logistical process expansion, classify its significance for corporate management, establish and discuss references to other business applications within a supply chain.
• Students can recognize, describe, plan and analyze the added value of modern technologies from the field of Industry 4.0 for business processes.
Students will be able to design extensions for standard software for a complex logistics process based on technical requirements.Students learn to evaluate their own work results.

Social competence

• Students learn to develop results in project groups
.
• Students learn how to operate and present themselves in an entrepreneurial environment.

Professional field orientation

• Students learn about currently relevant aspects of ERP systems and how to classify them in their professional environment
.
• Students learn about the use of ERP systems in the area of supply chain execution
• Students learn about the cooperation between different groups in a company and their roles in the course of a project.

Interdisciplinary methodological competence

• Students are able to present, justify and discuss designs, procedures and results
.

 

The formal participation requirements are governed by the applicable degree program regulations.

• Knowledge comparable to Bachelor's course ERPII (WIPB-45392) or Business Administration Applications (I9PB-46990)
• Knowledge of process representation, e.g. from the GPM module (WIPB-46894)

• CORSTEN, H., GÖSSINGER, R. & SPENGLER, T. S. (eds.) 2018. Handbuch Produktions- und Logistikmanagement in Wertschöpfungsnetzwerken, Berlin, Boston: De Gruyter Oldenbourg.
• DESTRADI, M., KIESEL, S., LOREY, C. & SCHÜTTE, S. 2019. Logistik mit SAP S/4HANA, Bonn, Rheinwerk Publishing.

• ZOELLNER, P., HALM, R., SCHAPLER, D. & SCHULZE, K. 2012. SAP EWM Technische Grundlagen und Programmierung, Galileo Press.

In this module, students deepen their skills in the design of software architectures for complex systems. Students learn how to design a scalable, robust and maintainable domain-driven software architecture by selecting and applying suitable principles, patterns and methods. The analysis and discussion of such software architectures is based on practical examples and concrete solutions from research projects.

Technical and methodological competence:

• The participants are able to differentiate basic principles of software design and transfer them to specific application scenarios.
The students are able to differentiate, analyze and apply central patterns at the macro and micro architecture level.The students know relevant tools and methods for domain-driven design and can combine and implement them appropriately in concrete application scenarios.The students can name and classify current research approaches to modeling software architectures.

Interdisciplinary methodological competence:

• The participants master the analysis of a complex problem and can break it down into sub-problems. In this way, they strengthen their skills in implementing a comprehensive task as part of a project over several weeks in a team.
Students learn methods for the interdisciplinary development of solutions, e.g. together with experts without a technical background.

Social skills:

• The participants develop and implement solutions cooperatively in a team
.
• They are also able to present, explain and discuss their ideas and solutions.

Professional field orientation:

• Students acquire knowledge to solve typical tasks in the field of software architectures. They can make well-founded design decisions and justify them.
• In addition, they gain experience in the use of essential software development tools, such as development environments or build management tools.

The module covers the following topics:

• Short repetition of the bachelor material on software design (e.g. design patterns according to Gamma et al., separation of concerns, layered architecture)
• In-depth aspects of software design:
  • Principles (e.g. loose coupling - high cohesion, SOLID)
  • Architectural patterns (e.g. ports and adapters, CQRS)
  • Methods (e.g. domain-driven design, WAM approach)
• Characteristics and patterns of modern architectural styles (e.g. modular architectures, event-based architectures, microservice architectures)
• Model-driven design, development and reconstruction of software architectures

• Evans E.; Domain-Driven Design: Tackling Complexity in the Heart of Software. Addison-Wesley; 2003
• Vernon V.; Domain-Driven Design kompakt. dpunkt; 2017
• Richardson C.; Microservice Patterns. Manning; 2018
• Starke G.; Effektive Softwarearchitekturen. Hanser Verlag; 8. Auflage; 2018
• Martin R. C.; Clean Architecture. Prentice Hall; 2018
• Goll J.; Entwurfsprinzipien und Konstruktionskonzepte der Softwaretechnik. Prentice Hall; Springer Vieweg; 2018
• Bass, Len, Paul Clements, and Rick Kazman. Software Architecture in Practice: Software Architect Practice. Addison-Wesley, 2012.
• Balzert H.; Lehrbuch der Softwaretechnik. Entwurf, Implementierung, Installation und Betrieb. Spektrum Akademischer Verlag; 3. Auflage; 2011
• Gamma E., Helm R., Johnson R., Vlissides J.; Design Patterns. Addison-Wesley; 1995
• Rademacher, Florian. A language ecosystem for modeling microservice architecture. Diss. 2022.

Students master the capital market theory models for determining the cost of capital. They understand the interdependencies between investment decisions, financing decisions, the cost of capital and firm value.

• Recognizing the relationship between risk and return
• Assessment of investment projects against the background of risk-adequate capital costs
• Identifying specific advantages and disadvantages of financing structures
• Determination of optimal project financing taking into account taxes and insolvency costs
• Application of specific project valuation methods

Self-competence:

• Solving a practice-oriented case study independently

The students' skills as method experts in process modeling are expanded in relation to the basic knowledge of a Bachelor's degree. Knowledge of the possibilities of working with reference models, the further use of process models (conversion of technical models into executable models) in downstream phases in the system implementation and selection process is available. The extensive aspects of process analysis have been developed. Knowledge of how a technically described example can be converted into a model using a graphical description method with the help of a tool for process modeling and analysis is available.

Technical and methodological competence:

• Recognizing the importance of business process management as a necessary component of a successful introduction of information systems
Apply various graphical modeling methods to business management tasks
• Developing a process model for business management tasks from practice based on different objectives
• Working with a tool for process modeling
• Explaining the framework conditions for the successful introduction of process management
• Describing and determining various aspects of process analysis
• Conducting process analyses
• Assess an actual process model and derive success factors (weak point analysis) for a target concept

Interdisciplinary methodological competence:

• Applying methods from business process management to business management issues in different operational functional areas

Self-competence:

• Developing a solution for a given question from business process management with independent argumentation

Social skills:

• Presenting a solution to a group using a presentation
• Moderating a discussion in a group

Professional field orientation:

• Practical training with various tools for process modeling and analysis
• Preparation for working as a counseling consultant in the field of business process management/introduction of standard application software
• Knowledge of the job description of a method expert, business architect, subject matter expert
.

Technical and methodological competence:

• First, central concepts of project management are introduced. The students' heterogeneous basic knowledge is taken up and continued. In particular, methods of project planning are deepened. Students are able to carry out a planning project.
Students are familiar with current project management standards.The students acquire knowledge of project management methods (in particular time and cost management).Students learn concepts of quality and risk management.They acquire knowledge of change and claim management.

Interdisciplinary methodological skills:

• The students recognize that project management methods are transferable to other tasks of an information systems specialist.

Self-competence:

• Selected project management methods are applied by the students themselves during the course.
The students take responsibility for work packages in the project to be carried out.

Social skills:

• Students learn special methods and tools that support cooperation and communication in a project (e.g. mind mapping) and how to link the tools. The methods and tools are also used in the course.
The students are able to apply the knowledge from all phases of the course, i.e. to select project management methods and tools for this complex project and apply them in a team.

Professional field orientation:

• The students know the tasks and job description of a project manager.

In this module, students gain an overview of the architectures of complex web applications and analyze their differences and areas of application. They learn how corresponding web applications can be implemented by selecting and using suitable client- and server-side technologies.

• The participants can analyze and differentiate between different architectures and central architectural patterns of web applications
The participants are able to derive a suitable architecture from a concrete problem and to determine and apply suitable web technologies for implementation.Students will be able to name, classify and apply important web standards and technologies.

Interdisciplinary methodological competence:

• The participants have mastered the analysis of a comprehensive requirement and can break it down into sub-requirements. They have experience of implementing sub-requirements over several weeks as part of an overall project in a team.
Students can classify, derive and implement software system architectures.

Social skills:

• The participants develop and implement solutions cooperatively in a team
.
• They are also able to present, explain and discuss their ideas and solutions.


Professional field orientation:

• Students acquire knowledge of typical tasks in web development and the application of specific web technologies.
In addition, they gain experience in the use of essential software development tools, such as development environments or build management tools.

The lecture covers the following topics:

• Brief review of the basics of building websites with HTML, CSS and JavaScript (Bachelor material)
• Consideration, analysis and differentiation of architectures of modern web applications:
  • Architectural patterns such as MVC and its variants (MVVM, MVP, etc.)
  • Request-based and component-based web frameworks
  • Single page applications, server-side rendering, client-side rendering
  • Reactive programming/streaming
• In-depth study of server-side technologies for the development of web applications (e.g. with Java, JavaScript)
• Deepening client-side concepts and technologies for the development of web applications (e.g. component-oriented development, state management, routing)
• Overview of current developments in web standards (e.g. web components, WebAssembly)

• Wolf J.; HTML5 und CSS3: Das umfassende Handbuch; Rheinwerk Computing; 4. Auflage; 2021
• Bühler P., Schlaich P., Sinner D.; HTML5 und CSS3: Semantik - Design- Responsive Layouts; Springer Vieweg; 2017
• Simpson K.; Buchreihe "You Don't Know JS" (6 Bände); O'Reilly; 2015
• Haverbeke M.; JavaScript : richtig gut programmieren lernen; dpunkt.verlag; 2. Auflage; 2020
• Simons M.; Spring Boot 2: Moderne Softwareentwicklung mit Spring 5; dpunkt.verlag; 2018
• Tilkov S., Eigenbrodt M., Schreier S., Wolf O.; REST und HTTP; dpunkt.verlag; 3. Auflage; 2015
• Kress, D.; GraphQL: Eine Einführung in APIs mit GraphQL; dpunkt.verlag; 2020
• Starke G.; Effektive Softwarearchitekturen. Hanser Verlag; 9. Auflage; 2020

Building on a basic understanding of cost and performance accounting, students are familiar with the performance-oriented model concept of IT controlling as opposed to the value-oriented approach. Students are familiar with the relevant methods of an operational and strategic IT toolbox and the new developments in IT, such as predictive or prescriptive analytics, platform companies and blockchain as part of the corporate business.

Technical and methodological expertise:

• Determine the semantic meaning of the basic terms of IT controlling
• Determine the special requirements of cost accounting for IT controllers
• Describe and explain important methods of strategic IT controlling, such as IT strategy, standardization, balanced scorecard, portfolio management and methods for determining value in IT
• Select and analyze strategic methods based on practical examples and case studies
• Explaining and developing selected operational tools of IT controlling, e.g. special aspects of IT cost and performance accounting, key performance indicator systems for IT, the importance of performance agreements in the form of service level agreements and decision-making bases for outsourcing processes
.
• Explain new IT technologies and their significance for company business models.

Self-competence:

• Developing a solution for a given question

Social competence:

• Presenting a solution in a group
• Moderating a discussion in a group

• Gadatsch, A., Masterkurs IT-Controlling, 5. Auflage, Wiesbaden 2015
• Gadatsch, A., IT-Controlling für Einsteiger, Praxiserprobte Methoden und Werkzeuge, Wiesbaden 2016

ergänzende Literatur:

• Buchta, D. u. a., Strategisches IT-Management, 3., überarbeitete und erweiterte Auflage, Wiesbaden 2009
• Gröner, U., Geschäftsprozessmanagement I, Online-Publikation, o. O. 2008, unter: http://www.shaker.de/de/content/catalogue/index.asp?lang=de&ID=6&category=181
• Krcmar, H., Informationsmanagement, 6. Aufl., Berlin et al. 2015
• Wöhe, G., Döring, U., Betriebswirtschaftslehre, 26. Auflage, München 2016