Data Science & Intelligent Analytics

The following topics are discussed in the course:

- Classic neural networks as a complement to classic data science algorithms (e.g. Random Forests, SCM, etc.)
- Convolutional artificial neural networks (CNN)
- Recurrent artificial neural networks (RNN, LSTM)
- Advanced artificial neural networks (GAN, FARM, BERT, CGAN, etc.)

The types of networks discussed are subject to constant change. For this reason, only a few network types are named here as examples. In particular, current network types are also discussed and applied in the course.

Students are introduced to the fundamental characteristics of big data. Particular attention is paid to how such data is handled, and the knowledge acquired is reinforced with examples. Suitable frameworks are presented for solving big data problems and are worked on with case studies in interactive workshops. Examples include:

- Apache Spark
- Apache Flink
- Apache Storm
- Apache Samza
- Apache Kafka

These frameworks are explained and applied using case examples. The centrally provided data labs can be used for this purpose.

The following topics are discussed in the course:

- Fundamentals of descriptive statistics
- Measurement system analysis
- Sample size determination
- Statistical process control
- Process control charts
- Process capability analysis
- Components of Variance analysis (COV)
- Review of the fundamentals of inferential statistics
- Determining root causes of errors through hypothesis testing (t-test, chi-square, ANOVA)
- Multiple regression analysis

The following sample topics are discussed in the course:

- Biology (e.g. genome research, medical diagnostic procedures, etc.)
- Physics (e.g. object recognition through image data processing, etc.)
- Chemistry (e.g. processing of data-intensive experiments, etc.)
- Sustainability
- Data-driven maintenance (e.g. predictive maintenance, digital twin)
- Data-optimized product design (e.g. designing product properties using ANN)
- Analysis of sensor data (e.g. obstacle detection, obstacle avoidance, prediction, etc.)
- Cloud-based IoT systems (data storage and collection)
- Sensor analysis via Raspberry Pi, Arduino, wireless systems

The following topics are discussed in the course:

- Reasoning approaches (goal trees, rule-based expert systems)
- Search approaches (depth-first, hill climbing, beam, optimal, branch and bound, A*, games, minimax, and alpha-beta)
- Constraint approaches (search, domain reduction, visual object recognition)
- Learning approaches (neural nets, back propagation, genetic algorithms, sparse spaces, phonology, near misses, felicity conditions, support vector machines, boosting)
- Representation approaches (classes, trajectories, transitions)
- Possible applications of artificial intelligence in different contexts
- Weak versus strong artificial intelligence

The following topics are discussed in the course:

- CRM at the strategic level
- CRM in process management
- CRM at the operational level (CRM software systems)
- Operational CRM
- Analytical CRM
- Communicative CRM

The following topics are discussed in the course:

- Presentation of different application-oriented analytics platforms (e.g. KNIME, RapidMiner, Grafana)
- Presentation of different cloud solutions for data analysis (e.g. Google Cloud, AWS, Azure)
- Applying the presented platforms using sample analysis datasets
- Discussion of the different approaches

The following topics are discussed in the course:

- Analysis tools with a visual focus, e.g. BI tools such as MS Power BI, Tableau, QlikView
- Visualization libraries, e.g. matplotlib.pyplot, ggplot2
- Rules of visual communication, e.g. Hichert SUCCESS

- Basic concepts: business process, workflow, BPMS, WFMS, RPA, etc.
- Selection criteria for workflow engines for process automation
- Architecture and integrations of workflows for process automation
- Overview of interprocess communication
- Transactional properties of processes, simulation and code generation
- Fundamentals of Microsoft Dynamics 365: modules and navigation, base entities and standard workflows
- Organizational and technical implementation with configuration and declarative programming

- Staff scheduling and resource management
- Contract management and financial management (cost and activity accounting)
- Quality management and quality assurance (reviews, audits, etc.)
- Employee information and communication
- IT strategy and IT portfolio management
- Risk management; IT governance and compliance
- ITIL, CoBIT
- Service level agreements and outsourcing
- Balanced scorecard methodology
- IT management tools (ADOit, etc.)

Introduction
- IoT architecture (e.g. reference models)
- Requirements for IoT systems
- IoT data transmission protocols
- Use of IoT in an industrial context (examples)
- Fundamentals of sensor technology
- Fundamentals of embedded systems

Implementation
- Approach to implementing IoT
- Prototype implementation of IoT
- Selection of sensors
- Collection, visualization and analysis of data
- Challenges in implementation

- Fundamentals of human–AI interaction
- Human cognition, mental models, decision-making behavior
- HCI principles for AI systems
- UX design for AI
- Interaction patterns (conversational UI, agents, multimodal interfaces)
- Prompt design, system messages, feedback loops
- Trust and user acceptance
- Explainable & Transparent AI (XAI)
- Types of explanation, user groups, design principles
- Usability and control of AI
- Human-in-the-Loop & Human-on-the-Loop
- Matching human-machine roles
- Task allocation, limits of automation
- Error prevention & recovery design
- Empathic & social aspects of AI interaction
- Social responses, trust
- persuasive and adaptive systems
- Methods & tools
- Usability testing for AI systems
- Prototyping tools
- Evaluation methods (task success, trust metrics, cognitive load)

The following topics are discussed in the course:

- The process of software engineering and project management for data-intensive applications
- Programming paradigms for use in the field of data science
- Effective and efficient data structures for data-intensive applications
- Tools and software ecosystems for developing and testing data-intensive software systems

In the lab, the content of the integrated lecture "Software Development 1" is deepened through practical exercises. The insights gained are discussed in the group and thus allow a deep understanding of the subject matter and a consolidation of the knowledge that was conveyed theoretically in the integrated lecture.

In the lab, the content of the integrated lecture "Statistical Learning 1" is deepened through practical exercises. The insights gained are discussed in the group and thus allow a deep understanding of the subject matter and a consolidation of the knowledge that was conveyed theoretically in the integrated lecture.

The following topics are discussed in the course:

- Characteristics of high-performance data systems (scalability, maintainability, reliability)
- Established concepts of data storage (relational model)
- Historical concepts of data storage (hierarchical model, network model)
- Modern concepts of data storage (wide-column model, graph model, key-value model, document model, column-oriented model)
- Database systems matching the models covered
- Scaling of data systems (replication and partitioning)
- Writing and reading in data systems (index structures, write strategies)

The following topics are discussed in the course:

- Statistical measures (point and interval estimators)
- Statistical test procedures
- Grouping algorithms (classification trees, agglomerative hierarchical clustering, etc.)
- Regression algorithms (regression trees, random forests, etc.)
- Associative algorithms
- Data preprocessing procedures (e.g. principal component analysis)

The following topics are discussed in the course:

- Design and implementation of problem-centered NoSQL databases (e.g. key-value stores, document stores, column-oriented data stores, etc.)
- Design and implementation of storage solutions for large volumes of data (big data)

In the lab, the content of the integrated lecture "Statistical Learning 2" is deepened through practical exercises. The insights gained are discussed in the group and thus allow a deep understanding of the subject matter and a consolidation of the knowledge that was conveyed theoretically in the integrated lecture.

The following topics are discussed in the course:

- advanced modeling procedures
- Ensemble methods
- Optimization of models

The following topics are discussed in the course:

- Architecture models for data-driven software development and systems.
- Integration models and paradigms for implementing complex, process-oriented software ecosystems for analytical and data-driven systems
- Application of proven design patterns for data-driven applications
- Design and implementation of efficient and scalable software systems for data-driven applications
- Testing of software applications (e.g. unit tests, integration tests, etc.)

In the lab, the content of the integrated lecture "Software Development 2" is deepened through practical exercises. The insights gained are discussed in the group and thus allow a deep understanding of the subject matter and a consolidation of the knowledge that was conveyed theoretically in the integrated lecture.

Fundamentals and modeling:
-Introduction to digital twins, their significance and areas of application
-Teaching the theoretical fundamentals and methods of modeling

Simulation and software:
-Overview of simulation techniques and their typical applications
-Getting to know various simulation software and practical exercises

Smart products and solutions:
-Creation and analysis of models for smart, communicating products
-Integration of digital twins into IoT systems and their advantages

Advanced concepts and applications:
-Introduction to advanced topics such as condition monitoring and predictive maintenance
-Discussion of the role of digital twins in future technology development.

Practical project:
-Planning and carrying out your own digital twin project to apply what you have learned

The content of this course is not fixed but is adapted to the trends currently prevailing. Sample topics may include:

- Current best-practice approaches and concepts in application areas (e.g. smart home, smart city, smart production, connected vehicles, etc.)
- Current best-practice approaches with regard to development processes and tools
- Current research and development activities and research and development results

The content of this course is not fixed but is adapted to the trends currently prevailing. Sample topics may include:

- New technologies in the field of big data processing
- Trends in programming languages in data analysis
- New concepts for processing data (e.g. data lake)
- New questions in the field of data science research
- New questions in the field of data science practice

The content of this course is not fixed but is adapted to the trends currently prevailing. Sample topics may include:

- New technologies in the field of AI engineering
- Trends in programming languages for AI
- New design concepts in the field of AI
- New questions in the field of AI research
- New questions in the field of AI

- Current developments in the field of enterprise application systems with specific reference to ERP systems and business process management
- Models, examples, best-practice cases

The following topics are discussed in the course:

- Intercultural competence
- Discussion with representatives from the field

The following topics are discussed in the course:

- Project management techniques (e.g. SCRUM)
- Project management tools in the field of data science (e.g. GitLab)
- Techniques for documenting requirements (e.g. Sophist)
- Mechanisms of gender roles, bias and structural disadvantage, as well as practical approaches to gender-inclusive design of work and learning environments
- Models and methods for workload and resource analysis (work-life balance)

The following topics are discussed in the course:

- Developing a holistic understanding of the subject areas (systemic management)
- Methods for generating innovative ideas (e.g. Systematic Inventive Thinking, Design Thinking)
- Project structures and management methods for the practical implementation of innovations (e.g. change management, conflict management)
- IT-supported project documentation

The following topics are discussed in the course:

- Data protection (e.g. GDPR)
- Privacy (e-Privacy Regulation)
- Handling data from an ethical/moral perspective
- Compliance

In this course, students work on a real, data-centered project along the entire data value chain (from collecting the data through its integration and storage to analyzing and making the data usable). This allows them to try out the skills they built up in the first two semesters in a real setting and to gain new insights.

Students are introduced to the areas of philosophy of science and academic work. The objectives of academic work are discussed and applied to their own research problems. As part of the course, students thus develop an initial exposé draft for a master's thesis.

The course accompanies students in the conception and preparation of their master's thesis. In the colloquium, the research question/hypothesis and the structure of the master's thesis are therefore presented and discussed. In addition, the academic methodology of the master's thesis is discussed and examined critically, and guidance is given on the formal design of the master's thesis.

Students independently design a project idea for their own master's thesis, describe it in the form of an exposé and submit it to the program management for approval. The students then work on the topic and write a master's thesis, which is submitted for assessment.