Data Analysis for Social Scientists
This statistics and data analysis course will introduce you to the essential notions of probability and statistics. We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.
This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively, but it is challenging. Students who are uncomfortable with basic calculus and algebra might struggle with the pace of the class.
|University:||Massachusetts Institute of Technology|
|Discipline:||Economics, Social Sciences|
|Workload per week:||8.0 h|
|Tags||data visualization , econometrics , estimation , experiments , forecast , machine learning , probability , randomized control trials , regression , statistics|