Signal and Noise: An Introduction to Statistics

How do numbers relate to the world? What insights can we derive from data? How do we separate signal from noise? This course is an introduction to statistical thinking and its applications to data analysis at a level accessible to a broad audience with no prior statistical background. 

We’ll learn and make intuitive the fundamental methods and concepts of data quantification: linear regression, logistic regression, probability distribution, hypothesis testing; statistical significance; and correlation vs. causation. 

We’ll examine, too, the role of data in the world—its omnipresence and utility for science, business, technology, and public policy. In what sense are we data-driven? Why do predictive models work and yet sometimes, unexpectedly, err? In what ways can statistical techniques be used, intentionally or not, to wildly mislead and convincingly support falsehoods and biases? In this course, we’ll not only learn fundamental statistical methods, but also examine the limitations of data, its predictive capacities, and its uses and abuses.

Note:

There *is* no physical Brooklyn Institute. We hold our classes all over (thus far) Brooklyn and Manhattan, in alternative spaces ranging from the back rooms of bars to bookstores to spaces in cultural centers, including the Center for Jewish History, the Goethe-Institut, and the Barnard Center for Research on Women. We can (and do) turn any space into a classroom. You will be notified of the exact location when you register for a class. 

Instructors will contact students approximately one week prior to the first class with reading assignments and details about the course location