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Instructors: Jun. Prof. Dr. Karsten Donnay
Event type:
Seminar / exercise
Org-unit: Politics, Administration & International Relations
Displayed in timetable as:
AM Big Data Analysis
Hours per week:
1,5
Credits:
3,0
Location:
Campus der Zeppelin Universität
Language of instruction:
Englisch
Min. | Max. participants:
5 | 30
Priority scheme: Standard-Priorisierung
Course content:
This block course provides a basic introduction to big data and corresponding quantitative research methods. The objective of the course is to familiarize students with big data analysis as a tool for addressing substantive problems. The course begins with a basic introduction to big data and discusses what the analysis of these data entails, as well as associated technical, conceptual and ethical challenges. Strength and limitations of big data research are discussed in depth using real-world examples. Students then engage in case study exercises in which small groups of students develop and present a big data concept for a specific real-world case. These exercises are designed to familiarize students with the format of big data and to gain a first, hands-on experience with potential applications for large, complex data in policy-relevant settings. There are no prerequisite requirements for this course.
Educational objective:
Students will be able to formulate research designs for addressing substantive social science problems using big data approaches. They will be able to judge the strengths and limitations of alternative approaches and have a good understanding of the concrete steps such a big data analysis would entail. The block course is intentionally designed as a primer for anyone interested in attaining a basic understanding of what big data analysis entails and therefore does not entail technical training for scripting etc.
Further information about the exams:
Performance in the course depends both on active participation in class and performance in the case study exercises. Evaluation will be based on:
- Active participation and contribution to class (20%)
- Written case study report (80%)
Admitted Aids:
Students are encourage to use any relevant sources or materials when compiling their case study reports.
Mandatory literature:
The following are recommended for anyone interested in background readings on big data written for scientific and general audiences. Recommended scientific readings and/or online resources for individual sessions will be provided with stable links in the course syllabus.
The first book is written for social scientists interested in conducting big data analysis and a useful guide for everybody interested in data science. The second book focuses primarily on possible downsides of algorithms and big data analysis in various domains. And the third book both provides an overview of big data, open data and data infrastructures and associated concepts as well as a discussion of potential shortcoming and (unintended) consequences of this paradigm shift for science and society.
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