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Linear Algebra for Data Science in R

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

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4 Hours15 Videos56 Exercises11,651 Learners4000 XP

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Course Description

Linear algebra is one of the most important set of tools in applied mathematics and data science. In this course, you’ll learn how to work with vectors and matrices, solve matrix-vector equations, perform eigenvalue/eigenvector analyses and use principal component analysis to do dimension reduction on real-world datasets. All analyses will be performed in R, one of the world’s most-popular programming languages.
  1. 1

    Introduction to Linear Algebra

    Free

    In this chapter, you will learn about the key objects in linear algebra, such as vectors and matrices. You will understand why they are important and how they interact with each other.

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    Motivations
    50 xp
    Creating Vectors in R
    100 xp
    The Algebra of Vectors
    100 xp
    Creating Matrices in R
    100 xp
    Matrix-Vector Operations
    50 xp
    Matrix-Vector Compatibility
    50 xp
    Matrix Multiplication as a Transformation
    100 xp
    Reflections
    100 xp
    Matrix-Matrix Calculations
    50 xp
    Matrix Multiplication Compatibility
    50 xp
    Matrix Multiplication - Order Matters
    100 xp
    Intro to The Matrix Inverse
    100 xp
  2. 4

    Principal Component Analysis

    “Big Data” is ubiquitous in data science and its applications. However, redundancy in these datasets can be problematic. In this chapter, we learn about principal component analysis and how it can be used in dimension reduction.

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Datasets

NFL Player datasetWNBA Massey Matrix datasetWNBA Point Differentials dataset

Collaborators

dcamposliz
David Campos
chester
Chester Ismay
shoninouye
Shon Inouye

Prerequisites

Introduction to R
Eric Eager Headshot

Eric Eager

Data Scientist at Pro Football Focus

Eric Eager is the VP of R&D at SumerSports. Prior to joining Sumer in 2022, he ran R&D at Pro Football Focus, where he analyzed data for all 32 National Football League teams and over 130 college football teams, along with every major media entity and thousands of subscribers. From 2012-2018 he was a professor in the Department of Mathematics and Statistics at the University of Wisconsin – La Crosse, where he published over 25 papers in mathematical biology and the scholarship of teaching and learning while securing more than $300,000 in National Science Foundation funding for undergraduate mentorship.
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