In this video, Siraj Raval does something that your middle school math teacher did not: make math fun, funny, and engaging.

Bloomberg takes a look at the unique role of data science in professional basketball.

From the description:

With her PhD in math, Ivana Seric had expected to wind up with a career in academia—but thanks to the growing use of statistical analysis in the NBA, she took a job with the Philadelphia 76ers instead. As a data scientist, she helps the team’s coaches devise smarter strategies to win.

I’ll never forget the time I first heard of non-Euclidean spaces. It made sense and no-sense all at the same time. Since making the switch into data science, I understood it better and its uses. However, I never really tried to visualize these spaces.

Fortunately(?), someone has created a rendering engine that lets you explore this space and surprise(!), it may have uses for VR.

Randomness comes up quite a lot in statistics. Statistics comes up a lot in data science. Therefore, I think you will all enjoy this video from MajorPrep about the math around randomness.

You’ll hear the term Bayes or Bayesian come up a lot in data science, but this video explores the theory with tennis balls and a table.

When I was in college one of my favorite subjects was Discrete Mathematics. In this video, Siraj Raval explains what it is and he does so very discretely. (Sorry, I couldn’t resist.)

The next time someone tells you that math has no practical application, tell them about the solved mysteries mentioned in this video.

A recent post on the math needed to do machine learning got me thinking and, when I get to thinking, I get to searching. I found this course on YouTube on Linear Algebra. In it, you’ll learn what linear algebra is and how it relates to vectors and matrices. Then look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally, learn at how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works.

Towards the end of the course, you’ll be able to write code blocks and encounter Jupyter notebooks in Python.

In this lecture from MIT, Jeremy Kepner talks about his newly released book, “Mathematics of Big Data,” which serves as the motivational material for the D4M course.

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