What is data? How is data generated? Classified? Cleaned? Stored? “Cooked?” What are machines “learning” from data? What do machines “predict” from data? Why does it matter?

This teaching narrative unpacks the key concepts related to data concerning their history, technical approaches, and implications for contemporary society.

The students will explore a combination of primary texts and theoretical approaches that discuss the consequences of data capture, storage, analysis, and prediction. Students will also work with case studies during hands-on data science labs to explore the theory learned in class.