23-24.14.232 Data Science for Socioeconomists [Englisch]

Lehrende: Prof. Dr. Ulrich Fritsche

Veranstaltungsart: Vorlesung + Übung

Anzeige im Stundenplan: Data Science

Semesterwochenstunden: 4

Credits: 6,0

Unterrichtssprache: Englisch

Min. | Max. Teilnehmerzahl: - | 200

Kommentare/ Inhalte:
This teaching lab for future socioeconomists introduces students to the world of data and its analysis, using many examples from business and society. Topics range from data management to data analysis using various machine learning methods, visualization and presentation of results, and critical reflection on data science. As fundamental tools, students will learn the basics of the R programming language and the light-weight markup language Markdown, as well as how to use Quarto notebooks to perform data analysis on their own in RStudio.

Lernziel:
By the end of the course, participants should be able to work on their own empirical research questions. In addition to the data literacy content, they will also learn helpful methodological approaches such as pair programming, peer review and agile working methods.

Vorgehen:
Lectures will be delivered in a hybrid format in English. In online synchronous exercises, students have the opportunity to apply what they have learned in groups, to discuss and clarify questions using University Hamburg’s Jupyter coding hub (https://code.min.uni-hamburg.de/hub/ ). They can also deepen their knowledge with the help of explanatory videos. The exercises are supported by student tutors.

Literatur:
-          James, G. (2021). An Introduction to Statistical Learning
-          Healey, K. (2019). Data Visualization: A practical introduction
-          Peng, R. (2022). R Programming for Data Science
 

Zusätzliche Hinweise zu Prüfungen:
Take Home Exam:

Time to process the exam: 120 minutes

Time frame in which the exam can be completed:

1. Exam: 19 February 2024, 3 pm - 7 pm
2. Exam: 19 March 2024, 3 pm - 7 pm

The examiner of your course will provide information about the hand out of examination tasks / assignments and their submission during the course of this semester, at the earliest after the STiNE Changes and Corrections Period.