Description

With the desire to learn more about computational social science, I’m embarked on a process to learn more about how computational approaches can be used responsibly and effectively in the study of people and their behavior. As such, I’ve enrolled in Computational Social Science 273L at UC Berkeley. In this course in this course, we will reach the following learning objectives:

• Proficiency with tools for reproducibility of research
• Conceptual understanding of machine learning methods, including strengths and weaknesses of various algorithms and their appropriate application to different kinds of prediction and classification problems
• Ability to apply these concepts and execute relevant methodologies on social science data in Python and correctly interpret results
• Familiarity with key empirical papers that apply computational social science methods to research

And we will covers topics such as:

• Reproducibility
• Transparency
• Ethics
• Machine learning
  • Regression
  • Classification
  • Clustering

This page will show new projects created throughout this course.