GESIS Training Courses
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Scientific Coordination

Dr.
Sebastian Wenz

Administrative Coordination

Jacqueline Schüller
Tel: +49 0221 47694-160

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Course 5: Data Science Techniques for Survey Researchers

About
Location:
Cologne / Unter Sachsenhausen 6-8
 
General Topics:
Survey Methodology, Data Analysis
Course Level:
Advanced
Format:
Summer School
Software used:
R
Duration:
5 days
Language:
English
Fees:
Students: 550 €
Academics: 825 €
Commercial: 1650 €
 
Keywords
Additional links
Lecturer(s): Fiona Draxler, Anna Steinberg, Malte Schierholz

About the lecturer - Fiona Draxler

About the lecturer - Anna Steinberg

About the lecturer - Malte Schierholz

Course description

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.
     
    For additional details on the course and a day-to-day schedule, please download the full-length syllabus.
     
    Organizational structure of the course
    The course is partly theoretical and partly practical. Each topic will be introduced in a lecture followed by practical hands-on exercises. Files written in R Markdown will be provided to help you execute the prepared scripts on your own computers and complete the assignments. The instructors will be available to assist and answer questions during the practical sessions.


    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.


    • Learning objectives

    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.


    • Prerequisites

  • General knowledge of statistics and statistical modeling (e.g., linear regression)
  • Prior experience with R (or similar syntax-based software).
  • We expect that you have basic data handling competencies in R and can understand R code.
    •  
    Software and hardware requirements
    You should bring your own laptop to successfully participate in the course.
    We expect you to have the following free and open-source software installed prior to the course:
  • R (https://cran.r-project.org/)
  • RStudio (https://posit.co/download/rstudio-desktop/)
  • Git (https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
    •  
    Moreover, you should create an account on GitHub, https://github.com/.
     
    We will inform you a few days before the course starts about recommended steps to set up your system. You should be able to access the internet and install additional packages during the course (Wifi is provided by GESIS).
     
    “For an introduction or refresher in R programming, you might consider enrolling in GESIS's two-day onsite course, “ Introduction to R for Data Analysis ” held in the first week of the Summer School in Cologne, or the four-day online workshop, “ Introduction to R ” offered in May.
     
    An alternative short self-paced online course we recommend can be found here: https://www.berd-nfdi.de/berd-academy/soon-data-science-with-r/
     
    Some other resources can be found here:
    https://rstudio.cloud/learn/primers
    http://www.statmethods.net/
    https://swirlstats.com/
    https://rmarkdown.rstudio.com/lesson-1.html (for R Markdown)


    Schedule

    Recommended readings