This course traces the historical precursors in the construction of knowledge and thought that are part of the contemporary emphasis on quantification, discrete numerical measurement, and predictive automated systems. The course examines the scientific revolution both as an historical event and a philosophical shift in the way truth claims are constructed and substantiated. We examine the evolution in the way truth and facts are constructed as this ethos intersects with the rise of bureaucratic institutions, including the corporation and the state. Along the way, we examine the logic of categories, the difficulty humans have in cognizing large numbers and statistical thinking, the role of data visualization in telling stories with/about data, and key problems in expert-driven knowledge production. We conclude the class by examining the contemporary turn towards predictive uses of large datasets. Knowledge production via prediction is a break from descriptive uses of data, even in schemas where descriptive data was used to support causal reasoning. Predictive implementations of knowledge production are fraught with the dangers of false positives, false negatives, and “true” positives & negatives drawn from training data that is laced with the social problems of the past (e.g. sexism, racism, elitism). What has not changed, however, is the way that ruling elites are harnessing data at a scale that enhances the potential of ‘control creep’ both at larger scales than we have previously seen and with more precise impacts on specific individuals than was previously possible.