Interactive Course

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.

  • 4 hours
  • 15 Videos
  • 56 Exercises
  • 2,968 Participants
  • 4,000 XP

Loved by learners at thousands of top companies:

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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. Matrix-Vector Equations

    Many machine learning algorithms boil down to solving a matrix-vector equation. In this chapter, you learn what matrix-vector equations are trying to accomplish and how to solve them in R.

  2. 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.

  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.

  2. Matrix-Vector Equations

    Many machine learning algorithms boil down to solving a matrix-vector equation. In this chapter, you learn what matrix-vector equations are trying to accomplish and how to solve them in R.

  3. Eigenvalues and Eigenvectors

    Matrix operations are complex. Eigenvalue/eigenvector analyses allow you to decompose these operations into simpler ones for the sake of image recognition, genomic analysis, and more!

  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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Devon

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Lloyd's Banking Group

Louis

“DataCamp is the top resource I recommend for learning data science.”

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Harvard Business School

Ronbowers

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Decision Science Analytics @ USAA

Eric Eager
Eric Eager

Data Scientist at Pro Football Focus

Eric Eager is a data scientist for Pro Football Focus, where he analyzes data for all 32 National Football League teams and over 40 college football teams. Before joining PFF in 2018, he was a professor in the Department of Mathematics and Statistics at the University of Wisconsin – La Crosse, where he published over 20 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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