MethodsNet + RSS
MethodsNet + RSS

Big Data: Data Collection, Scraping and APIs in R (RSS1.08) - Closed

This one-week course covers advanced techniques of web scraping and algorithm auditing and is tailored for social scientists. Participants will enhance their existing skills in data collection and analysis, focusing on ethical scraping practices of dynamic websites and auditing algorithms for social research. The course is aimed at those with foundational knowledge in R and basic web scraping.

    General

    Closed: registration no longer possible. We have some alternatives for you: 

    We would like to recommend that you consider joining our course on ‘Big Data: Introduction to Text Analysis’, which includes some of the same topics as your course and is also in R. Please feel free to contact the instructor (Bruno de Castanho Silva : b.paula.castanho.e.silva [at] fu-berlin.de (b[dot]paula[dot]castanho[dot]e[dot]silva[at]fu-berlin[dot]de)) for further information about the course, including what topics are covered and the level of the course. 

    Another recommendation is to consider joining “Use of AI for Political Analysis”, which focuses on using Large Language Models’ API’s that would have featured prominently in your course. Feel free to contact the instructor (Ethan Busby) for information about the course and the level of models covered in that class.  

    Collaboration

    Watch what our participants say about their experience!

    MethodsNet + RSS

    (Social) Research Methods courses

    Course list
    Course list

    Overview courses and disciplines

    Course list

    Cannot join us this year? 

    We can keep you informed about the 2025 course program! Do you want to broaden your knowledge in 2025 over courses about sustainability, law, research methods & skills, data science and more. Get an email when the new proposal is ready. Because you have part to play!

    Keep me informed

    Starting date

    17 June 2024, 9 am
    Educational method
    On-site
    Main Language
    English
    Sessions
    17 June 2024, 9 am - 21 June 2024, 5 pm
    Teacher(s)
    Aleksandra Urman
    Unique code
    RSS1.08

    Factsheet

    Type of education
    Course
    Entry requirements
    See the requirements in cost and admission
    Study load (ECTS)
    2
    Result
    Certificate
    Organisation
    Radboud Summer School

    This specialized course is designed for social scientists seeking to deepen their skills in web scraping and learning algorithm auditing using R. Participants will learn how to efficiently scrape complex and dynamic web data, including handling JavaScript-heavy sites, overcoming common scraping problems and working with APIs.

    The course begins with a refresher on basic web scraping concepts, ensuring all participants are on the same page. Following this, we will cover advanced scraping methods, covering scraping of dynamically loaded JavaScript-heavy websites and techniques for bypassing common scraping obstacles (e.g., avoiding getting detected and blocked by websites; creating user profiles with specific characteristics, etc). A significant portion of the course is dedicated to algorithm auditing. This includes understanding the fundamentals of this technique that allows evaluating the functionality and impact of black-box algorithms used in social media, search engines, LLM-based chatbots and other platforms. 

    Participants will learn how to collect and analyze data to audit these algorithms, uncovering potential biases and analyzing related ethical implications. The course will cover practical examples and case studies using advanced web scraping techniques and working with APIs. Throughout the course, hands-on exercises and projects will allow participants to apply their learning in real-world scenarios. Ethical considerations, such as respecting privacy and adhering to legal frameworks, will be a recurrent theme. By the end of the course, participants will be equipped with advanced skills in web scraping and a solid foundation in algorithm auditing, enabling them to conduct sophisticated data-driven research in the social sciences.

    Total package & social events

    Aleksandra Urman

    Aleksandra Urman
     

    Dr. Aleksandra Urman is a Postdoctoral researcher with Social Computing Group, University of Zurich. In her work, she applies and develops diverse computational social science techniques to study online political communication and algorithmic bias. Her core methodological competencies include web scraping, algorithm auditing, computational text analysis, statistical analysis, and network science approaches.

    Costs

    • Regular: €1049 (application deadline 1st of May)
    • Student & PhD's: €699 (application deadline 1st of May)

    Includes: your course, short morning and late afternoon courses, coffee and tea during breaks, a warm lunch every day, Official Opening, MethodsNET Café (including some drinks and snacks) Official Closing (with some drinks and snacks) and a 1-year (2024 calendar year) free membership as MethodsNET regular member.

    Excludes: transport, accommodation, social events and other costs. 

    Discounts and Scholarships

    Apply for this course

    Admission

    Level of participant: 

    • PhD
    • Postdoc
    • Professional

    Admission requirements: 

    R programming skills (intermediate) -Familiarity and some experience with basic web scraping techniques

    Admission documents: 

    None

    Apply for this course