## Visualizing Matrices

Matrices are everywhere in Data Science. In fact, they are everywhere in Computational Science. Stanford’s Margot Gerritsen has a wonderful video on Visualizing Matrices.
First, she walks us through the basic structure of a matrix. Yes, a matrix consists of rows and columns. But it should be viewed as a system of equations with an underlying structural relationship among the coefficients.
Then,...

## On Adaptive Learning: A Partial Response to Audrey Watters

In “The Algorithmic Future of Education” Audrey Watters offers a sweeping critique of adaptive learning, arguing that “robot tutors” (her phrase) don’t benefit learners, they are not anything new under the sun, and that, worst of all, they represent a cunning ploy by industry (in league with administrators and managers) to “subjugate labor” and to create “austerity”. According...

## Lorena Barba’s Keynote: Computational Thinking is Computational Learning

“If you can do mathematics with a dynamic technology instead of with a static one, then perhaps you can do real mathematics instead of denatured mathematics and thereby open the possibility of a Samba School effect.” —Seymour Papert
The term computational thinking was coined by MIT’s Seymour Papert and popularized by Carnegie Mellon’s Jeannette Wing. In a series of papers and talks...

## Joseph Blitzstein and “The Soul of Statistics”

Reasoning with “uncertainty” is at the core of analytics. The science of uncertainty itself has two faces: probability and statistics. Probability allows us to calculate the “likelihood” and “unlikelihood” of events. If God, Death, and Taxes are the only certainties, then the rest of life lies squarely in the realm of probability. Statistics allows us to reason from...