After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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.
Prerequisites
Curriculum
Course outline
1
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.
- Motivations50 XP
- Creating Vectors in R100 XP
- The Algebra of Vectors100 XP
- Creating Matrices in R100 XP
- Matrix-Vector Operations50 XP
- Matrix-Vector Compatibility50 XP
- Matrix Multiplication as a Transformation100 XP
- Reflections100 XP
- Matrix-Matrix Calculations50 XP
- Matrix Multiplication Compatibility50 XP
- Matrix Multiplication - Order Matters100 XP
- Intro to The Matrix Inverse100 XP
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.
R
Linear Algebra for Data Science in R
Course
Complete
