Linear Algebra for Data Science in R-Datacamp
Linear Algebra for Data Science in R-Datacamp ranks 6th in the list of best online Linear Algebra courses. Linear algebra is a crucial set of tools in applied mathematics and data science. You'll learn how to work with vectors and matrices, solve matrix-vector equations, perform eigenvalue/eigenvector analyses, and use principal component analysis to reduce dimension on real-world datasets in this course.
All analyses will be carried out in R, one of the most popular programming languages in the world. In this course, you will learn the fundamentals of linear algebra, such as vectors and matrices, eigenvalue and eigenvector analyses, and so on. You will also learn how to use principal component analysis to perform dimension reduction on real-world datasets.
This course use the R programming language for performing all analysis. There are 4 chapters in this course
- 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.
- 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.
- 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!
- Principal Component Analysis
- “Big Data” is ubiquitous in data science and its applications. However, redundancy in these datasets can be problematic. In this chapter, you learn about principal component analysis and how it can be used in dimension reduction.
Who Should Enroll?
- Those who know R programming language.
Rating: 4.2/5.0
Enroll here: datacamp.com/courses/linear-algebra-for-data-science-in-r
