Introduction to Data Science with Python

The Course covers foundational aspects of data collection, visualization, and machine learning, using basic Python and key packages like pandas, numpy and scikit-learn. Data used will cover a broad array of sources, from "native Web" data such as Social Media data to more "traditional" survey data.

Course leader

Dr. Arnim Bleier, Dr. Fabian Flöck, Dr. Juhi Kulshrestha (GESIS, Germany)

Target group

Participants will obtain profound knowledge about typical data types and structures encountered when dealing with digital behavioral data, state-of-the art data analysis methods and tools in Python, and they will learn how this approach differs from those typically encountered in survey-based or experimental research. This will enable them to identify benefits and pitfalls of these data types and methods in their field of interest and will thus allow them to select and appropriately apply data analysis and machine-learning methods for large datasets in their own research. The knowledge obtained in this course provides a starting point for participants to investigate specialized methods for their individual research projects.

Course aim

In this course, we aim to provide an introduction into data science for practitioners with Python. In particular, we want to impart basic understanding of the main methods and algorithms and understand how these can be deployed in practical application scenarios, focusing on the analysis of digital behavioral data (or "digital traces of humans") found on the Web.