Scientific Coordination
Dr.
Marlene Mauk
Tel: +49 221 47694-579
Marlene Mauk
Tel: +49 221 47694-579
Administrative Coordination
Noemi Hartung
Tel: +49 621 1246-211
Tel: +49 621 1246-211
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Introduction to Computational Social Science with Python
About
Location:
Mannheim B6, 4-5
Mannheim B6, 4-5
Course duration:
09:00-16:00 CEST
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Keywords
Additional links
Lecturer(s): Milena Tsvetkova, Patrick Gildersleve
Course description
The course provides an introduction to the basic computational tools, skills, and methods in Computational Social Science using Python. Python is the most popular programming language for data science, used widely in both academia and the industry. Students will learn to use common workflow and collaboration tools, design, write, and debug simple computer programs, and manage, summarize, and visualize data with common Python libraries. The course will employ interactive tutorials and hands-on exercises using real social science data. Participants will work independently and in groups with guidance and support from the lecturers. The practical exercises are designed to demand more autonomy and initiative as the course progresses over the five days, culminating in an open-ended group project in the last afternoon session.
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.
Target group
Participants will find the course useful if they:
Learning objectives
By the end of the course participants will:
Organisational Structure of the Course
The course will consist of two three-hour-long sessions each day. The morning session will use interactive instruction to introduce participants to the topic, demonstrate the new methods, and facilitate discussion. The afternoon session will make use of guided hands-on exercises with real-world data to practice the new material. Participants will work individually, in pairs, and in groups, and the lecturers will be available throughout both sessions for consultation and support.
Prerequisites
This is an introductory course, and no prior experience with programming is required. Basic understanding of statistics and some scripting experience (e.g., from building web pages or statistical analysis programs such as Stata or R) will be helpful but not needed.
For those who would like a primer or refresher in Python, we recommend taking the online workshop “Introduction to Python” that takes place from 04-06 September 2023.
Software and hardware requirements
Participants require a laptop computer with Anaconda and git installed. Some time will be allocated on the first day of the course to install Anaconda and git.
Agenda
Monday, 11.09. |
What is CSS? Setting up your workflow Introduction to programming with Python |
Tuesday, 12.09. |
Understanding control flows Abstraction and decomposition with functions Object-oriented programming with classes Modules and libraries |
Wednesday, 13.09. |
Handling social data Accessing web data Text analysis with nltk |
Thursday, 14.09. |
Introduction to pandas Manipulating pandas DataFrames Machine learning with scikit-learn |
Friday, 15.09. |
Basics of visualization Plotting data with Matplotlib and Seaborn Network analysis with networkx |