OFFERED COURSES
| Term | Type | Title | Workload |
|---|---|---|---|
| WS19 | Lecture | Applied Natural Language Processing and Text Mining | 4 SWS, 6 ECTS |
| Lecture | Information Visualization | 4 SWS, 6 ECTS | |
| Seminar | Selected Topics in Data & Knowledge Engineering | 2 SWS, 3 ECTS | |
| SS19 | Lecture | Blockchain – Technology and Applications | 4 SWS, 6 ECTS |
| Lecture | Information Retrieval | 4 SWS, 6 ECTS | |
| Seminar | Selected Topics in Data Science | 2 SWS, 3 ECTS | |
| WS18 | Lecture | Introduction to Computer Science for Non-Computer Science Majors | 4 SWS, 6 ECTS |
| Lecture | Key Competences in Computer Science | 4 SWS, 9 ECTS | |
| Seminar | Selected Topics in Media Informatics | 2 SWS, 3 ECTS | |
Assignment of Courses to Degree Programs
The table shows the degree programs and course areas for which the credits from our courses can be used.
(Click on the table to enlarge it or click here to download the table as a PDF.)
References to DKE courses in study regulations:
Information Technology, BSc
Information Technology, MSc
Industrial Engineering, BSc Electrical Engineering
Industrial Engineering, MSc Automotive
Industrial Engineering, MSc Energy Management
Industrial Engineering, MSc Information Technology
[WS19] Applied Natural Language Processing and Text Mining
By completing the the course, participants will obtain the knowledge and skills to solve a wide range of applied problems in Natural Language Processing. To achieve this goal, the participants will get to know successful methods for solving sub-problems, such as text representation, information extraction, text mining, language modeling, and similarity detection. The participants will understand the conceptual requirements of specific NLP tasks and be able to devise approaches to address these tasks in practice. The participants will be able to assess the strengths and limitations of state-of-the-art NLP approaches and to propose solutions for interdisciplinary NLP problems.
Workload: 4 SWS (2 Lecture / 2 Exercise), 6 ECTS
| Day | Time | Periodicity | Duration | Room | Type |
|---|---|---|---|---|---|
| Thu. | 10:00 - 12:00 | weekly | 17.10.2019 - 30.01.2020 | FC E.10 (00.10) | lecture |
| Thu. | 12:00 - 14:00 | weekly | 24.10.2019 - 30.01.2020 | FC E.10 (00.10) | exercise |
[WS19] Information Visualization
The course participants will acquire the knowledge and skills necessary to visualize a wide range of data for analysis, exploration, and information purposes. The participants will learn the fundamentals of human perception, design and interaction principles as well as elemental visualization techniques necessary to create visualizations suitable for the given type of data and the intended use case. The participants will also know the requirements that different data types and levels of complexity impose on the visualization as well as how to evaluate the quality of information visualizations. Much of the data covered in the course is abstract, i.e., the data has no spatial reference and thus cannot be mapped trivially to geometric visuals. Examples of abstract data include survey results, database contents, or genome information. The participants will be challenged with data from many more applications in industry, business, science and everyday life.
Workload: 4 SWS (2 Lecture / 2 Exercise), 6 ECTS
| Day | Time | Periodicity | Duration | Room | Type |
|---|---|---|---|---|---|
| Wed. | 10:00 - 12:00 | weekly | 16.10.2019 - 30.01.2020 | FC E.10 (00.10) | lecture |
| Wed. | 12:00 - 14:00 | weekly | 16.10.2019 - 30.01.2020 | FC E.10 (00.10) | exercise |
[WS19] Selected Topics in Data & Knowledge Engineering
Seminar participants will explore current research trends and established approaches in the field of Data Science field. The participants can choose to complete either the theoretical or the practical research project track as part of this seminar.
Workload: 2 SWS, 3 ECTS
| Day | Time | Periodicity | Duration | Room | Type |
|---|---|---|---|---|---|
| Wed. | 14:00 - 16:00 | weekly | 16.10.2019 - 30.01.2020 | FC E.10 (00.10) | seminar |