Seminar: Visualizing InnovationDozent:innen: Michel Schilperoord
Kurzname: S Techniksoziologie
Voraussetzungen / OrganisatorischesZielgruppe:
- Bachelor Studierende im Studiengang Soziologie (Kernfach) [po 2011, 2016]
- Bachelor Studierende im Studiengang Soziologie (Beifach) [po 2016]
- Bachelor Studierende im Studiengang Wirtschaftspädagogik (Schwerpunktfach "Sozialwissenschaften")
Stellung im Studiengang:
- B.A. Soziologie Kernfach: Modul "Wahlveranstaltung Soziologie (Vertiefung)" [po 2011]
- B.A. Soziologie Kernfach: Vertiefungsveranstaltung im Modul "Vertiefungs- und Wahlveranstaltungen 1" [po 2016]
- B.A. Soziologie Kernfach: Vertiefungsveranstaltung im Modul "Vertiefungs- und Wahlveranstaltungen 2" [po 2016]
- B.Sc. Wirtschaftspädagogik Schwerpunktfach "Sozialwissenschaften": Modul "Aufbaumodul Soziologie"
Computational Social Science (CSS), a young but growing research area at the intersection of social science, computational science and complexity science, refers to the use of (advanced) computational approaches in studying social phenomena. The main CSS areas are automated information extraction systems (e.g. automated text analysis), social network analysis, social geographic information systems (GIS), complexity modeling, and social simulation (e.g. agent-based simulation models). Skills of computational social scientists are built on foundations of statistical analyses done in Stata, SPSS, or another such program, through learning/exploring new skillsets uncommon in the social sciences that are developed by computer scientists and computational statisticians. These include network analysis, natural language processing and machine learning techniques, and the programming languages R and Python, all associated with doing "data science" in the "age of big data".
This seminar pulls together ideas from the introductory-level Computational Social Science course "Bridge from Statistics to Data Science”, and the "Simulating Knowledge Dynamics and Innovation Networks (Introduction to the SKIN platform)” course. In the course, students will be introduced to applications of data science that can be relevant to their studies. Particular attention goes to visualization applications for open data, helping them in mapping and simulating innovation and entrepreneurship in big data domains (e.g. health care, transportation, government). It opens up questions with regard to opportunities and limitations that characterize the state-of-the-art for each computational method and visualization technique, with methodological focus in network analysis, natural language processing and machine learning. It will also assist in gaining hands-on experiences for programming (basic) applications of data science in R and Python, building on previously learned skills for doing (basic) statistics in Stata. Along hands-on class work, it will further assist in developing appreciation for the principal techniques in “big data viz” and common elements of data science workflows, for instance: data exploration, modelling and simulation, and communication of data science results.
This course covers a survey of practical examples of how CSS researchers with a foundation in statistics/Stata can apply (basic) methods of Computational Social Science for achieving a better understanding of certain social and economic issues and problems. On the theoretical side, it will provide an overview of CSS and foundational knowledge on its common methods, their differences, with key literature systematically reviewed. On the practical side, it will provide guidance for reasoning about which types of data science methods may be suitable for application to certain social and economic issues and problems, and how to make choices with regard design and implementation of a data science project.
Course Requirements and assignments:
Assignment 1: Pre-class preparation
This assignment is about producing excerpts (1 page per text) from the text book.
Each student is asked to agree with the instructor on seven excerpts taken from the different sections of the textbook.
The excerpts will be uploaded on Jugostine five days before the block seminar dates.
Assignment 2: Class presentation
This assignment is about presentation of a topic in class.
Each student is asked to discuss his/her choice with the instructor, and prepare a session from the syllabus. In certain cases, this can be done in cooperation.
Assignment 3: Data Visualization project
This assignment is post-class work on a data science project concerning one topic of the syllabus (mostly the one chosen for presentation). The students need to present a short abstract and the contents structure for their project to the instructor 10 days after the block seminar dates. The deadline for essay submission is the end of Term.
Recommended reading list:
Cioffi-Revilla C. (2014) Introduction to Computational Social Science (Texts in Computer Science). London: Springer.
Zusätzliche InformationenWeitere Informationen zu den Vertiefungs- und Wahlveranstaltungen finden Sie hier: https://www.soziologie.uni-mainz.de/pruefungsordnung2016ba/
|20.04.2022 (Mittwoch)||12:15 - 13:45||Online
|27.04.2022 (Mittwoch)||12:15 - 13:45||Online
|04.05.2022 (Mittwoch)||12:15 - 13:45||Online
|11.05.2022 (Mittwoch)||12:15 - 13:45||Online
|18.05.2022 (Mittwoch)||12:15 - 13:45||Online
|25.05.2022 (Mittwoch)||12:15 - 13:45||Online
|01.06.2022 (Mittwoch)||12:15 - 13:45||Online
|08.06.2022 (Mittwoch)||12:15 - 13:45||Online
|15.06.2022 (Mittwoch)||12:15 - 13:45||Online
|22.06.2022 (Mittwoch)||12:15 - 13:45||Online
|29.06.2022 (Mittwoch)||12:15 - 13:45||Online
|06.07.2022 (Mittwoch)||12:15 - 13:45||Online
|13.07.2022 (Mittwoch)||12:15 - 13:45||Online
|20.07.2022 (Mittwoch)||12:15 - 13:45||Online