Explore the fundamentals of data structure analysis in this 25-minute lecture from MIT's 18.S191 Fall 2020 course. Delve into topics such as matrix rank, data visualization using Julia's Plots.jl package, and statistical concepts like standard deviation. Learn about centering datasets, analyzing correlated data, and understanding Principal Component Analysis (PCA). Discover how to visualize higher-dimensional data and apply these concepts to create a simple recommendation engine. This comprehensive lecture, presented by David Sanders, offers a blend of theoretical concepts and practical applications in data science using the Julia programming language.
Overview
Syllabus
Introduction.
Flags.
Rank of a Matrix.
How do we know the data is close to a structured, rank-1 matrix?.
Plotting data using "Plots.jl" in Julia.
Size of the data cloud.
Centre the dataset/"de-meaning".
How wide is this dataset? Use Standard Deviation in Julia.
Correlated data.
Principal Component Analysis.
Visualizing in Higher Dimension.
Application: A simple recommendation engine.
Taught by
The Julia Programming Language
