Curriculum Master Business Informatics
Business Informatics
Agile HR Management & Cross Culture Management
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | BINM1HRCIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 4 |
| Examination character | immanent |
Learning Outcomes:
Alumni place HR theories in the context of digitalisation and agility. They have the ability to formulate an HR strategy with a focus on digitalisation and agility and to translate this into operational HR measures. Based on the application of classic HR management instruments, their digital and agile design is known and applicable for the alumni. These instruments are placed in the context of the New Work approach. Furthermore, alumni have intercultural sensitivity and understanding for other cultures in and outside the company. They have basic assessment, argumentation, reflection and analysis skills with regard to ethical or sustainable contexts.
Superior module:
Designing Structures
Module description:
xxx
Analytics & Knowledge Discovery
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | BINM1AKDIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Alumni will be able to apply classical methods of exploratory data analysis to different types (numerical, categorical, textual) of data. They are able to implement a knowledge discovery process (data mining, information retrieval, structure discovery methods), reduce the dimensionality of the data, identify clusters and visualise them accordingly. The course focuses on unsupervised learning.
Superior module:
Data Science & Analytics
Module description:
xxx
Data Literacy, -Awareness & -Security
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | BINM1LASIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Alumni understand the role of data in the digital transformation and develop an understanding of data sources and data-generating processes as well as methods and approaches for data acquisition. They can handle data appropriately and record, collect, manage and transform it accordingly. They derive requirements for data governance in the company from this. In doing so, they take into account requirements for data security and privacy in relation to compliance, legal framework conditions and technical implementation strategies. They are able to assess data quality and integrity. They have the competence to analyse and interpret data in a business context with software tools in terms of value and costs. They present data and analysis results in a suitable form for this purpose. They are able to assess whether and how business issues can be solved or supported with the information obtained and which legal and ethical issues arise in the process.
Superior module:
Digital Economy 1
Module description:
xxx
Data Science
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | BINM1DSCIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Alumni know the types and components of data science projects, can describe their structure and name the corresponding positions and designations of employees. They understand the concepts behind data, models and algorithms and use technical language to describe them. They discuss the suitability of data collections or data acquisition processes for specific tasks. They are able to apply methods and algorithms to extract information from data in different representations (numerical, categorical, one-hot or textual). They know methods for collecting, cleansing and visualising data in order to develop an understanding from an application perspective. Following the further design cycle for supervised learning, they can implement feature extraction and sampling of training and test data, parameterise and train selected (simple) classifiers and evaluate their performance. For this purpose, they use state-of-the-art development environments and scalable technologies and are able to argue selected solutions in terms of content.
Superior module:
Data Science & Analytics
Module description:
xxx
Informatics Technologies
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | BINM1IFTIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 3 |
| ECTS Credits | 4 |
| Examination character | immanent |
Learning Outcomes:
Alumni are able to design and implement distributed software systems and realise distributed data management and distributed software-based services. They can use current component technologies and business-relevant middleware systems and use methods and tools of platform-independent software development.
Superior module:
Business Software Conception & Design 1
Module description:
xxx
International Economic Relations
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | BINM1IWBIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Alumni have a sound knowledge of the changes and risks in the business environment that companies and business decision-makers face. The focus is on the changing framework conditions of entrepreneurial activity: the internationalisation of economic activities, deeper market integration, the emergence of new competitors and the importance of digitalisation for international economic relations. The alumni are able to recognise, analyse and evaluate the risks and opportunities for management resulting from the international environment in a decision-oriented manner.
Superior module:
Challenging Economic & Societal Conditions
Module description:
xxx
New Business Models
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | BINM1NBMIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Alumni are able to design digital business models based on digital products and digital processes. In doing so, they recognise how digitalisation enables or even conditions these developments. They can analyse the requirements for (company) structures, interfaces, system boundaries and policies in the digital as well as in the analogue area. They relate the possibilities of information technology to this and are able to derive implications for companies, markets, customers and employees. They are able to define the central value creation process, orientate themselves on models with a generic character such as the circular or sharing economy and evaluate the long-term success and sustainability of digital innovations. They discuss metrics that can be used to evaluate and accompany implementation. They can visualise, formalise and communicate these business models.
Superior module:
Digital Economy 1
Module description:
xxx
Software & Process Notations
| Semester | 1 |
|---|---|
| Academic year | 1 |
| Course code | BINM1SPNIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
The alumni have the competence to develop formalised descriptions of different artefacts of software development as well as of economic workflows and networked processes. They can use the common UML diagram types for system development and extend the notation, for example, by forming profiles. You are able to use appropriate CASE tools and evaluate methods and tools of platform-independent software development. They master abstraction concepts of model-driven software development.
Superior module:
Business Software Conception & Design 1
Module description:
xxx
Business Architecture
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | BINM2BATIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 4 |
| Examination character | immanent |
Learning Outcomes:
Alumni are able to design the entrepreneurial embedding of new business models, integrate the latter into existing structures if necessary and thus develop hybrid process and organisational models. They can map this embedding with digital governance methods: Within the framework of strategic alignment, they show which opportunities arise through the use of technology, for example through platformisation, cloudification and digital communication. By implementing appropriate approaches (for example, through Privacy & Security by Design, Agility by Design or by developing precise metrics for the assessment of structures and processes), they contribute to establishing compliance. They are able to develop a concept for data governance (such as the introduction of data democracy and corresponding data management) on the basis of technological implementation options.
Superior module:
Digital Economy 1
Module description:
xxx
Designing Value Creation Systems
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | BINM2VCSIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
The alumni receive a sound overview of the methods, models and optimisation approaches of value creation networks. At the end of the module, they are able to analyse value creation processes and identify corresponding optimisation potentials. The knowledge acquired in the course about the planning and design of value creation networks enables them to recognise the fundamental connections between corporate strategy, organisation and individual value creation processes. The alumni can identify the process parameters relevant for optimisation, know about their interactions and can independently develop solution approaches to support the operational implementation of strategic objectives.
Superior module:
Designing Structures
Module description:
xxx
Innovation Economics & Digitalization
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | BINM2IODVO |
| Type | VO |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | final |
Learning Outcomes:
The alumni are able to assess the micro- and macroeconomic implications of innovations for industrial dynamics, competition and growth and accordingly know the framework conditions for business management decisions. In addition, they can precisely distinguish the economic effects of the new digital technologies in the course of the platforms and also know how to determine the criteria for success. In this way, the abundantly vague talk about possible digital "scaling" is placed on the secure foundation of network effects and a well-founded application-oriented knowledge is acquired.
Superior module:
Challenging Economic & Societal Conditions
Module description:
xxx
Machine Learning
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | BINM2MLGIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Alumni understand the consequences and limitations of choosing a particular machine learning model in the context of statistical learning theory and in relation to the no-free-lunch theorem. They are able to select appropriately from known algorithms, parameterise them and evaluate them with respect to their complexity. During the training process, they can recognise overfitting and underfitting and counteract them with suitable countermeasures. They have the knowledge to select suitable machine learning models for different types of data (numerical, texts, images) and tasks (classification, representation learning, object recognition).
Superior module:
Data Science & Analytics
Module description:
xxx
Project 1: Ideate, Design, Implement, Reflect
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | BINM2PR1PT |
| Type | PT |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
The alumni possess implementation competences acquired in several projects in changing groups on learning contents from the areas of Digital Economy, Data Science & Analytics and Business Software Conception & Design. In the second semester, these short projects focus primarily on the area of ideation and the design of new business models and their mapping in companies and IT, as well as on data awareness and the optimal use of effects of the digital transformation. The alumni are able to apply established methods (for example, the design thinking process) in this area. During the attendance period, coaches from the above-mentioned areas and in the area of presentation & soft skills are available to the alumni. Through their support and feedback, the alumni acquire the ability to present projects in a way that is appropriate for the target group and have practised confident presentation. They are able to face a discussion and are able to take the different roles of the stakeholders in the processes in perspective. They have the ability to thematise the change processes associated with the introduction of technology and to bring them into a discourse.
Superior module:
Project 1
Module description:
xxx
Robust & Explainable AI
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | BINM2REAIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
The alumni deal with explainable and interpretable models of artificial intelligence (XAI) and can apply decision trees and their extensions as a form of them. This enables them to build robust systems whose predictions and decisions are comprehensible. The alumni understand how to interpret the influence of individual features on the result and communicate the model decisions. Furthermore, they can optimise the models in terms of their resource consumption through appropriate feature selection and/or model thinning while maintaining high prediction quality. They can analyse the impact of unbalanced, biased or noisy data on trained systems in terms of fairness or robustness.
Superior module:
Data Science & Analytics
Module description:
xxx
Software Architecture Integration
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | BINM2SAIIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
The alumni are able to evaluate contemporary software architectures and can soundly argue architecture decisions for development and integration projects. They apply software design patterns as well as architecture patterns (especially enterprise integration patterns) and can make informatic abstraction methods comprehensible and usable for involved stakeholders. They recognise innovation-relevant issues and independently develop suitable solution concepts in order to systematically manage a high degree of technical-methodical heterogeneity.
Superior module:
Business Software Conception & Design 1
Module description:
xxx
Software Engineering & Operations
| Semester | 2 |
|---|---|
| Academic year | 1 |
| Course code | BINM2SEOIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 3 |
| ECTS Credits | 4 |
| Examination character | immanent |
Learning Outcomes:
The alumni understand the various task fields and activities within the framework of the software development process and the productive operation of software and systematically master the challenges of organising different business-relevant software projects. The alumni are able to assess process models, can develop these independently and thus independently drive forward the conception, implementation and monitoring of professional software projects and the associated productive operation.
Superior module:
Business Software Conception & Design 1
Module description:
xxx
Big Data & Cloud Computing
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3BDCIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Alumni are able to master the technical and organisational challenges of big data processing and apply methods and techniques of data-intensive software development for this purpose. They can use common big data frameworks, understand how to use the transdisciplinary aspects of cloud computing and communicate its technological foundations. Furthermore, they are able to implement selected case studies of data-intensive business applications.
Superior module:
Business Software Conception & Design 2
Module description:
xxx
Business Analytics & Financial Modelling
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3BAFIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
The alumni understand the importance of data and its analysis possibilities in the context of controlling and financial management as well as for operational control. After successfully completing the courses, alumni can solve practical problems relating to operational and financial issues using relevant methods, instruments and programmes.
Superior module:
Designing Processes
Module description:
xxx
Business Process Management
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3BPMIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 4 |
| Examination character | immanent |
Learning Outcomes:
In an environment of increasing digitalisation and the associated dynamisation, alumni have the competence to analyse, design and implement business processes in companies with the aim of achieving a balance between stability and agility. They deal with the interaction between strategy, change and process management in the context of digitalisation within companies. They are able to use methods of business process management (e.g. using BPMN) to design change processes with the aim of a resilient and agile company organisation and to implement them in terms of software technology. Alumni are able to establish a data awareness culture and support the formation of modular, interdisciplinary and independently acting teams (employees as intrapreneurs) for the implementation of digital business models. They are able to promote continuous delivery through agility-by-design approaches (agile strategy map) and the use of metrics to map and control processes.
Superior module:
Digital Economy 2
Module description:
xxx
Digital Customer Management
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3DCMIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
At the end of the course, alumni know which tasks CRM managers have to deal with in the context of market-based strategy development and its (digital) implementation. The alumni are able to formulate goals in customer management and measure their effects using operative market research. The participants in the seminar are able to use market research instruments in a targeted manner in order to understand customer needs.
Superior module:
Designing Processes
Module description:
xxx
Ethics & Sustainability
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3ENKIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 1 |
| ECTS Credits | 1 |
| Examination character | immanent |
Learning Outcomes:
The alumni are sensitised in dealing with morals (moral ideals, entrepreneurial striving for profit) and ethics and are able to bring these concepts to practical implementation through experiences gained in concrete case studies. In doing so, they are able to reflect on the basic understanding of why dealing with ethical principles can be important for a company and to design the transition of the learned theoretical approaches into operational decision-making processes and thus their integration into practical everyday business life.
Superior module:
Digital Business Ethics & Responsibility
Module description:
xxx
Master Seminar
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3MASSE |
| Type | SE |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
The alumni are able to independently develop goal-oriented topics for scientific papers, they demonstrate the ability to build up scientific lines of argumentation and understand the importance of methodical procedures. They are capable of networked thinking and synthetic synopsis. They know the publication life cycle including the review process. Furthermore, they are able to assess quality aspects of scientific work in terms of content, form and structure.
Superior module:
Master Thesis & Master Exam
Module description:
xxx
Project 2: Ideate, Design, Implement, Reflect
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3PR2PT |
| Type | PT |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Following on from the corresponding course in the 2nd semester, the alumni deepen their competence in implementing the acquired knowledge in concrete subtasks of complex projects, reflecting on the courses "Business Architecture" and "Business Process Management". In doing so, they are able to solve technical tasks and map them in software and are able to process and document this accordingly in the setting of (agile) IT projects. They are able to address the area of tension between continuous delivery and agility on the one hand, and requirements for plannability and compliance on the other hand in the light of the organisational psychological challenges of agile settings.
Superior module:
Project 2
Module description:
xxx
SP: Smart Production & Logistics
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3SPLIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | German |
| SWS | 5 |
| ECTS Credits | 8 |
| Examination character | immanent |
Learning Outcomes:
Based on the technical knowledge, alumni expand their business competences in order to be able to support digitalisation projects in companies or to initiate and manage them themselves. They are thus able to recognise all relevant aspects of a digitisation project and they know about the essential success factors. They can initiate, plan and implement digitisation projects independently. Above all, they also acquire the necessary social and methodological skills for the implementation of digitisation projects, including the associated opportunity management tools.
Superior module:
SP: Digital Transformation in Operations & Supply Chain Management
Module description:
xxx
SP: Foundations of IT Security
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3FISIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | German |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Alumni acquire knowledge and practical skills in the field of operation and design of extended, secured communication networks. They understand potential threats to network infrastructures and know countermeasures. The alumni are able to practically implement countermeasures against current threats.
Superior module:
SP: Networking, Security & Privacy
Module description:
xxx
SP: Network Reliability & Virtualization
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3NRVIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | German |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Alumni can plan and implement reliable, high-performance IP networks, they can evaluate and optimise networks with regard to their reliability. They can plan, implement and optimise IP multicast networks. They are fundamentally familiar with BGP and can carry out basic BGP configurations. You are familiar with current network technologies from the areas of enterprise networking, data centre networking and service provider networking. You have insight into current developments in the field of network technology (e.g. Software Defined Networks (SDN), Programmable Dataplanes (e.g. P4) and Next-Gen SDN).
Superior module:
SP: Networking, Security & Privacy
Module description:
xxx
SP: Deep Learning
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3DLGIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
The alumni know both basic and current approaches and methods from the areas of deep learning and representation learning and are able to apply them to data sets with suitable toolboxes. In practical tasks, they examine the model construction and the choice of model parameters and decide on the use of pre-trained models in terms of transfer learning. They know methods of semi-supervised learning and data enrichment to optimise effectiveness on small data sets with domain knowledge (Small Data Challenge). They parameterise the respective learning algorithms and apply them to data sets with optimal use of hardware and software resources. They are able to develop innovative applications with these methods and know the limits and areas of application of the respective algorithms.
Superior module:
SP: New Technologies for Applied Artificial Intelligence
Module description:
xxx
SP: Natural Language Processing
| Semester | 3 |
|---|---|
| Academic year | 2 |
| Course code | BINM3NLPIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Alumni are able to apply so-called attention-based models for natural language processing and implement suitable networks for applications in areas such as machine translation and sentiment analysis in social networks. Building on previously acquired skills in preprocessing text data, they are able to use contextualised text representations and complex network architectures for this purpose. They are able to determine network parameters and design in a problem-adequate manner and know the limits and application areas of the respective algorithms.
Superior module:
SP: New Technologies for Applied Artificial Intelligence
Module description:
xxx
Digitization & Responsibility
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | BINM4DRPIL |
| Type | IL |
| Kind | Compulsory |
| Language of instruction | English |
| SWS | 2 |
| ECTS Credits | 3 |
| Examination character | immanent |
Learning Outcomes:
Alumni know the opportunities and challenges for responsible companies in the digital world. They can contribute to the public discourse themselves and encourage companies to proactively address digital ethics and social/ecological implications and to successfully embed them strategically and operationally.
Superior module:
Digital Business Ethics & Responsibility
Module description:
xxx
Lecture Series
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | BINM4RVGRC |
| Type | RC |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 1 |
| ECTS Credits | 1 |
| Examination character | immanent |
Learning Outcomes:
The alumni learn about current application scenarios in the field of business informatics, reflect on the effects of the use of digital technologies together with those affected and stakeholders and are able to transform these insights into experiential knowledge for their future work.
Superior module:
Digital Business Ethics & Responsibility
Module description:
xxx
Master Exam
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | BINM4MPGDP |
| Type | DP |
| Kind | Compulsory |
| Language of instruction | German |
| SWS | 0 |
| ECTS Credits | 2 |
| Examination character | final |
Learning Outcomes:
The alumni are able to present the hypotheses and solution approaches developed in the Master's thesis in relation to the technical requirements of the task from the field of business informatics and to defend them discursively. They are able to establish and argue cross-references to course contents of the study programme.
Superior module:
Master Thesis & Master Exam
Module description:
xxx
Master Thesis
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | BINM4MARIT |
| Type | IT |
| Kind | Diploma/master thesis |
| Language of instruction | German |
| SWS | 0 |
| ECTS Credits | 19 |
| Examination character | immanent |
Learning Outcomes:
The alumni can independently prepare written work and proceed scientifically and systematically. In addition to analysing and presenting problems, they are able to recognise goals, formulate hypotheses and critically question them. They develop the Master's thesis oriented towards the specialisation in terms of content. The alumni can argue and justify their approach scientifically.
Superior module:
Master Thesis & Master Exam
Module description:
xxx
SP: Digital Supply Network Collaboration
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | BINM4DSNIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | German |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Based on the technical knowledge, alumni expand their professional competences to include social and methodological competences in order to be able to support cross-company digitisation projects or initiate and manage them themselves. The alumni know the importance of transparency, communication and coordination in the supply chain. They know the challenges and necessary framework conditions and can develop optimisation approaches themselves. The alumni acquire the necessary tools to be able to assess how information and data are exchanged within a SC, which problems and effects can arise in the process and know the corresponding solution approaches. They are able to develop new ideas on how the data of a supply chain can be monetised, how effort and revenue can be evenly distributed and how a joint business model can ultimately be developed.
Superior module:
SP: Digital Transformation in Operations & Supply Chain Management
Module description:
xxx
SP: Secure Network Operations & Analytics
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | BINM4NOAIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | German |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Alumni know current approaches to the organisational integration and management of IT security. They are familiar with the process of creating security policies and know procedures for ensuring compliance with them, as well as ensuring secure operation through security information and event management (SIEM). The alumni know current procedures for implementing security concepts in large network infrastructures. They also know how the secure operation of these infrastructures can be checked by collecting data on security and performance and can apply advanced methods for evaluating and analysing this data.
Superior module:
SP: Networking, Security & Privacy
Module description:
xxx
SP: Current Trends in AI
| Semester | 4 |
|---|---|
| Academic year | 2 |
| Course code | BINM4CTAIL |
| Type | IL |
| Kind | Elective |
| Language of instruction | English |
| SWS | 3 |
| ECTS Credits | 5 |
| Examination character | immanent |
Learning Outcomes:
Together with researchers and experts, alumni develop and discuss new applications and technologies in the field of artificial intelligence. They are able to study scientific articles and deal with challenges and approaches to solutions in companies. They can reflect on the impact of technology and its social and ethical implications.
Superior module:
SP: New Technologies for Applied Artificial Intelligence
Module description:
xxx