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As we explored the content of the DVD series more carefully (realizing more fully the consequences of it being 7 years old), and as we looked through books and articles written for practitioners in Data Science, we concluded that neither was sufficient for helping us to understand how this explosion in analytic capabilities is affecting our lives and the lives of people we encounter. We badly needed material that was more recent and more topical than the DVD series and which was aimed at a consumer, citizen, web-surfing audience rather than material made to teach people how to DO data science.
Thus, our plan has been to develop the course using both selections from the DVD series (and its useful structuring of topics) and newer materials (video and text) that we will have spent a month uncovering. Our goal is to create a course that will be engaging and reflect ways in which Big Data is affecting our class participants’ own lives right now.
The particular interests of the two co-moderators are quite different: Shier’s is in the diverse mathematical and statistical approaches now being used to analyze complex and massive data that in the past two decades have revolutionized the digital information environment. Becker’s is in how everyday people can exploit those new data structures but also how they are being used by businesses and other social institutions to influence people’s behaviors and attitudes—for good and for ill. The course will meander back and forth across these two major aspects of Big Data in our lives.
Week 2: What are patterns in data and what is simply random? How to distinguish cause-and-effect from mere correlation?
Week 3: Searching on the Web: From all the billions of web pages, how does Google search make a pretty good guess about what you are looking for? (The Google "PageRank" algorithm) How do similar "recommendation systems" (like Netflix or Pandora) work? How do you find what you want (rather than what companies want you to find)?
Week 4: What happens to the information you provide to websites like Facebook? Audience-targeting: what websites sell to advertisers: namely, YOU!
Week 5: Hugely informative graphical representations made possible by "big data". Also, methods of ranking sports teams.
Week 6: How the exponential increase in the quantity of information created new methods for analyzing data. What do they mean by "machine learning"? "Training computers to think like humans"
Week 7: How does big data enable sports teams to better evaluate athletes and improve their winning strategies?
Week 8: How are data analytics used to better understand people in ways that produce social benefits—not for selling more widgets?
Week 9: How does big data enable a better understanding of human culture, how babies acquire language, what words people are speaking (i.e., speech-to-text)—in other words: machine learning for understanding human speech?
Week 10: How do computers recognize what is in an image; for example, how do they identify people's faces?
Week 11: Some machine learning algorithms: Regression, classification, strategy-game-playing by computers, "training computers to think like humans"
Week 12: More advanced machine learning algorithms: clustering, association, anomaly detection, dimensionality reduction, social networks, "deep learning," neural networks (maybe if we think about it again, we'll understand it!)
Week 13: What good is all this? What about privacy? Transparency? Security? And do these even work? How to improve what big data does for people and society.