Aim and approach

This course provides a strong methodology foundation in applying data science methods for social sciences. In the end of the course, the student knows how to (a) conceptually apply data science approaches in work and (b) knows how to code machine learning using R or Python language.

This course focuses on

  • conceptual understanding of applying data science approaches through discussing papers which have used a particular approach.
  • hands-on skills of conducting these analysis with data sets

By the end of the course, students can

  • conduct data pre-processing both with quantitative and qualitative datasets
  • apply unsupervised machine learning methods to quantitative and qualitative data and draw social science relevant conclusions from the results
  • apply supervised machine learning methods to quantitative and qualitative data and draw social science relevant conclusions from the results
  • discuss the benefits and challenges of using data science methods for social science research methods and skills to apply these methods in students’ own research domain.

If you have quiestions about the course, please contact us via grp-dcm-teaching@helsinki.fi .

Prerequisite

Before the first class, student should master bascis or Python or R and know how to

  • working with variables, for-loops and if-structures
  • opening files and writing files
  • calling functions or methods

Course materials

  • Chapter "Algorithmic Data Analysis" from Coding Social Science. Understanding and Doing Computational Social Science (shared via email) and prepeare by going through the exercises.
  • In class coding activities, install needed packages from 00 Setup Python or 00 Setup R

Suggested additional reading

  • Hastie, T., Tibshirani, R., & Friedman, J. The Elements of Statistical Learning. Elements. New York, NY: Springer New York.

Course evaluation

Course is evaluated as pass/fail. To pass, you need 125 points. Each student choose what learning activities they want to engage tio help them in their learning process and combine different approaches and modules.

  • Attending class discussion: 5 points per each class
  • Writing a response to data science article of your choice discussing its methodological choices: 5 points per each response
  • Doing the class activity and writing a reflection diary based on it: 10 points per each activity
  • Taking a paper of your choice which is not using data science methods and introduce how you would use data science approaches to redo that paper: 10 points per paper
  • Taking a paper of your choice which is not using data science methods and do write a replication study which used data science methods: 25 points per paper
  • Writing an empirical article (with introduction, theory, methods etc.) which utilises two methods discussed in the class: 80 points per article
  • Writing an empirical article (with introduction, theory, methods etc.) which utilises one method discussed in the class: 60 points per article
  • Writing a brief analysis of a research problem of your choice with these methods: 15 points per article
  • Propose your own activity here

(Please note that point scales migth still change before the first class.)

Syllabus

30.8. Introduction and Social science research questions and data science

Supplementary reading

30.8. Dictionary based methods and Working with Textual Data

Supplementary reading

31.8. K-means and cluster analysis

31.8. Topic models

1.9. Support vector machines and Naive Bayes

1.9. Decision trees and random forests

2.9. Association rules

2.9. Technical activities

3.9. Personal course plan development

3.9. Future Outlook