STIA-442 Sr Sem: Big Data & Politics
Spring for 2017-2018
Rajesh Veeraraghavan & Atul Pokharel
Big Data and its Politics

Talk of data is everywhere. This hands-on course provides a foundation for rigorous, reasoned use and critique of statistical techniques in real-world applications. We will also develop an ability to critically engage with the politics of data by thoughtfully analyzing it. Students will learn about three inseparable aspects of data: its techniques, its politics and its ethics. First, students will learn critical concepts in programming using Python and statistical inference through analysis of real-world data, with a focus on spatial datasets. This will require learning exploratory data analysis, inferential statistics and linear regressions. The course will develop these skills from the ground up, starting with the proofs of basic theorems. Second, students will develop a critical understanding of data in concrete, real world social settings. This includes understanding the design of studies such as Randomized Control Trials, the choices that are made in data collection and its analysis, and the institutional dimensions of data production. Third, students will become aware of the politics of production, use and deployment of data. Students will become fluent with the concepts of legibility, exclusion, discretion and participation including the possibilities and limits of “crowdsourced” data.

Learning goals:

Gain a technical understanding of statistical methods. Become fluent in their interpretation and gain exposure to advanced techniques for understanding data.
Understand the individual and institutional choices that are made in the production, collection and analysis of data. Learn how to match techniques to real world problems.
Develop a critical understanding of the politics of spatial data.
Acquire a foundational understanding of spatial data analysis in Python.

Prerequisites:
Familiarity with multivariable calculus and programming is helpful. Those without it will have opportunities to learn the foundational concepts. Instructor permission required.
Credits: 3
Prerequisites: None
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