Data Science & Intelligent Analytics

The following topics are covered in the course:

- Classical neural networks as a complement to classical 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 network types discussed are subject to constant change. For this reason, only a few network types are mentioned here as examples. In particular, the course also covers and applies current network types.

The following exemplary topics are covered 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 characteristics using artificial neural networks)
- 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, and wireless systems

The following topics are covered 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)
- Applications of artificial intelligence in various contexts
- Weak versus strong artificial intelligence

The following topics are covered in the course:

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

The following topics are covered 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
- Principles of visual communication, e.g., Hichert SUCCESS

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 through examples. To solve big data problems, suitable frameworks are presented and worked on in interactive workshops using case studies. Examples of these include:

- Apache Hadoop
- 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 covered 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

In the lab, the content of the integrated lecture "Software Development 1" is deepened through practical exercises. The insights gained are discussed as a group, thereby providing a thorough understanding of the subject matter and consolidating the knowledge conveyed theoretically in the integrated lecture.

The following topics are covered 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 to and reading from data systems (index structures, write strategies)

The following topics are covered in the course:

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

In the lab, the content of the integrated lecture "Statistical Learning 1" is deepened through practical exercises. The insights gained are discussed as a group, thereby providing a thorough understanding of the subject matter and consolidating the knowledge conveyed theoretically in the integrated lecture.

The following topics are covered 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 data volumes (big data)

The following topics are covered 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 the development and testing of data-intensive software systems

The following topics are covered 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 "Statistical Learning 2" is deepened through practical exercises. The insights gained are discussed as a group, thereby providing a thorough understanding of the subject matter and consolidating the knowledge conveyed theoretically in the integrated lecture.

In the lab, the content of the integrated lecture "Software Development 2" is deepened through practical exercises. The insights gained are discussed as a group, thereby providing a thorough understanding of the subject matter and consolidating the knowledge conveyed theoretically in the integrated lecture.

The following topics are covered in the course:

- Advanced modeling techniques
- Ensemble methods
- Optimization of models

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

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

The following topics are covered in the course:

- Intercultural competence
- Discussion with practitioners from the field

The following topics are covered 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 the gender-inclusive design of work and learning environments
- Models and methods of workload and resource analysis (work-life balance)

The following topics are covered 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 covered in the course:

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

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

In this course, students work on a real, data-centered project along the entire data value chain (from data collection through integration and storage to the analysis and utilization of the data). This allows them to apply the skills built up in the first two semesters in a real-world setting and to gain new insights.

Students independently develop a project idea for their own master's thesis, describe it in the form of a proposal, and submit it to the program director for approval. Students then work on the topic and write a master's thesis, which is submitted for assessment.

The course supports students in designing and preparing 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 examined and critically reviewed, and guidance on the formal design of the master's thesis is provided.