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Instructors: Prof. Dr. Eva Alice Christiane Bittner; Marten Borchers
Event type:
Seminar
Displayed in timetable as:
Smart City HH
Hours per week:
4
Credits:
6,0
Language of instruction:
German
Min. | Max. participants:
- | 20
Comments/contents:
The seminar Data-driven Solutions for the Smart City Hamburg (2DSC2 Hamburg) deals with different concepts from the field of Smart City and the development of solutions for real existing challenges, which are provided by cooperation partners. Smart City describes intelligent and innovative cities, in which citizens are in the focus and a community with a high quality of life and sustainable resource consumption is aspired. The seminar will use business intelligence methods to analyze and visualize processes and data as well as artificial intelligence (AI) and machine learning methods to develop solutions for real-world challenges. This may involve prototype development as well as organizational restructuring and/or (partial) automation.
Prerequisites
A basic knowledge of business intelligence, data science, and artificial intelligence is required for participation in this course. In addition, all students should have basic knowledge in software development. In the seminar, Python is preferred, but other languages are not excluded, and alternatives are possible depending on the skills available in the groups.
Learning objectives:
The participants will work in groups of four to five students with the cooperation partners to identify challenges and work on them in a problem-specific way. The didactic approach corresponds to that of inquiry-based learning, so that students have a high degree of influence on the content and can follow their intrinsic interests and contribute individual competencies. During the seminar the acquisition and deepening of the following competences is aimed.
Core competences
- Methods for collecting data & information and dealing with incomplete, compromised, or outdated data.
- Methods for the analysis and visualization of data (Data Science)
- Artificial Intelligence (AI) and Machine Learning (ML) in Python or Java (DL4J) for e. g. content classification and analysis.
- Business Process Management with BPMN
Other competences
- Corporate Communications
- IT-supported project management
- Presenting results
- Scientific writing
Didactic concept:
In the first four to six weeks, content from the areas of urban planning, smart city, business intelligence, data science and machine learning are taught. From week four, group work also begins, and the cooperation partners are introduced, who contribute real use cases that are then worked on in groups. The group work will be mostly self-organized and will be supervised by the instructors with regular appointments. After 2/3 of the semester the intermediate results are presented and after the end of the lecture period the results of the groups are presented in the plenum.
Literature:
Relevant literature will be provided at the beginning of the course.
Additional examination information:
The seminar will be graded, and the following performances are crucial.
- Active participation and independent work (ungraded)
- Interim presentation of results (ungraded, knockout criterion).
- Presentation of the final results of the group work (graded)
- Final report on the results including developed prototypes or similar. (graded)
The lecture conditions will be presented and discussed at the beginning of the session.
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