Data Science Techniques for Survey Researchers

About

A variety of digital data sources are providing new avenues for empirical social science research. To effectively utilize these data for answering substantive research questions, a modern methodological toolkit paired with a critical perspective on data quality is needed. This course will introduce state-of-the-art data science techniques that are suited for collecting and analyzing digital behavioral data, so-called "big data", and traditional survey data. In addition, aspects of data quality and error frameworks for digital (big) data sources will be discussed.

Specifically, the course will cover the following topics and techniques:
- New forms of data (e.g., social media data, sensor data, etc.) and their quality,
- Web scraping and APIs,
- Git and GitHub,
- Databases and SQL, and
- Machine learning for social scientists: Regularized regression, Tree-based methods, Clustering, Text mining & LLMs

After the course, you will have a profound understanding of important methods from the data science toolkit for collecting and analyzing the mentioned data types. Moreover, you will be able to apply these methods and techniques in your research using statistical software R

Course leader

Fiona Draxler, University of Mannheim, Anna Steinberg and Malte Schierholz, Ludwig-Maximilian-Universität Munich

Target group

You will find the course useful if:
- you are interested in learning some fundamental techniques in data science,
- you want to collect and work with digital behavioral data, be it administrative data or data found online,
- you want to understand what machine learning is

Course aim

By the end of the course, you will:
- understand the challenges involved in analyzing digital behavioural data,
- know the promises and benefits of (supervised) machine learning,
- be able to use (supervised) machine learning for data analysis,
- have learnt the key metrics used to assess the data quality for gathered data types

Fee info

The rates include the tuition fee, course materials, the academic program, social and plenary program, and coffee/tea breaks