This repository contains content from the MIT School of Architecture and Planning Big data and social analytics online short course. The Jupyter notebooks in this repository are provided to students in a hosted environment and should be used in combination with the video and other supporting material provided on the online campus.

Learn about the key terms and concepts related to big data and social physics as you explore the rules, laws, and policies that apply to the practice of big data analysis.

Learn about how humans can be understood by looking at communication streams, idea flow, social networks, and social learning, and discover various sources of data collected through personal sensors.

Explore the basics of data dynamics and data interrogation, and use basic statistical and visual data interrogation methods to analyze a data set.

Engage with the basic concepts of peer networks, network theory, the structure of networks, and graph clustering.

Progress to second-order analysis and discover the difference between correlation and causation, as well as techniques for differentiating between them.

Understand the concepts of social influence and social incentives, their impact on behavior change, and the importance of applying privacy-protecting methods to sensitive data.

Investigate problem set examples that cover application of big data insights in Healthcare, HR tech, Telco and Marketing. Combine what you have learned in Modules 1 to 6 to conduct a full cycle analysis on a large data set.

Examine problem set examples that look at the application of data in context and showcase data policy in action, and recommend interventions to be taken as a result of your analysis in Module 7.

Notebook Contributors: Andre Voges, Mieszko Manijak, Gorden Jemwa, Arek Stopczynski, Xiaowen Dong, and Yves-Alexandre de Montjoye.