Intro and Foundations of Data Science I

Explore the foundations of data science in this 58-minute lecture from the Simons Institute's Data Science Boot Camp. Delve into key concepts presented by David Woodruff and Ravi Kannan of Microsoft Research India, covering topics such as sums of independent random variables, distributions in data science, dimension reduction techniques, and singular value decomposition. Learn about length-preserving projection, projection with Gaussian vectors, and the Random Projection Theorem. Gain insights into exponential and power law distributions, as well as means separated by O(1) standard deviations. Enhance your understanding of fundamental data science principles and their applications in this comprehensive overview.

Intro
A Brief Description
Bootcamp Overview
Sums of independent random variables
Distributions in Data Science
The Theorem
Corollaries
Exponential, Power Law Distributions
Dimension Reduction-two methods
Length Preserving Projection
Projection with Gaussian Vectors
Random Projection Theorem
Means Separated by O(1) Standard Deviations
Singular Value Decomposition