Course
Linear Algebra for Data Science in R
IntermediateSkill Level
Updated 08/2022
RProbability & Statistics4 hr15 videos56 Exercises4,000 XP20,809Statement of Accomplishment
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Prerequisites
Introduction to R1
Introduction to Linear Algebra
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.
Linear Algebra for Data Science in R
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FAQs
Do I need a math background to take this linear algebra course?
No advanced math is needed. This beginner-level course teaches linear algebra concepts from the ground up in R, starting with vectors and matrices before moving to more advanced topics.
How is principal component analysis used in this course?
In Chapter 4, you apply PCA to real-world datasets for dimension reduction, learning how to handle redundancy in big data by extracting the most important features.
What practical applications of eigenvalues and eigenvectors are covered?
Chapter 3 shows how eigenvalue and eigenvector analyses decompose complex matrix operations into simpler ones, with applications in image recognition and genomic analysis.
Is this course useful for someone pursuing machine learning?
Yes. Many machine learning algorithms rely on matrix-vector equations, eigenvalue decomposition, and PCA, all of which are core topics covered here.
What tools and language does the course use?
All work is done in R. You use built-in R functions for matrix operations, solving equations, eigenvalue analysis, and performing principal component analysis on datasets.
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