Wrap-Up

1. Congratulations!

Congratulations, you have completed an Linear Algebra for Data Science in R.

2. Chapter 1 - Vectors and Matrices

In Chapter 1 you were introduced to and worked with two of the fundamental elements of linear algebra, vectors and matrices, and how they interacted with each other. Matrix-vector Calculations can help you take vectors and transform them into other vectors, which can help you do things like rate sports teams or websites. Matrix-matrix calculations can help you take matrices, which are just vectors of vectors, and transform them into other matrices, which is a big aspect of deep learning models like neural networks.

3. Chapter 2 - Matrix-Vector Equations

In Chapter 2 we learned what matrix-vector equations were, and how to solve them. This helped us rate WNBA teams at the conclusion of the 2017 season (go Minnesota!). Most machine learning models boil down to a matrix-vector equation of some sort, and the key is understanding that you're trying to answer the question of whether a collection of vectors can create a certain vector, and if this creation is unique.

4. Chapter 3 - Eigenvalues and Eigenvectors

In Chapter 3 we learned what eigenvalue/eigenvector problems were and how to solve them. The essence of eigenvalues and eigenvectors is being able to decompose a matrix operation into the linear combination of a bunch of scalar multiplications. This applies well to computer vision, genomics, and many additional topics.

5. Chapter 4 - Principal Component Analysis

In the last chapter, we learned why PCA is useful, and applied it to a multivariate data set for the National Football League draft. PCA is the standard for elementary dimension reduction, and can uncover substantial elements of your data for use in data visualization, unsupervised learning (e.g. clustering), supervised learning, and more.

6. Going Further

Now that you have some experience with both linear algebra and R, you are on a firm foundation to start exploring more amazing data science concepts via DataCamp moving forward. Examples of subsequent courses are - Introduction to Data - Working with Data in the tidyverse - Foundations of Probability in R - Exploratory Data Analysis - Data Visualization with ggplot2 (Parts 1 and 2) Additionally, dig into some of the many case studies we have at DataCamp as well!

7. Thank You!

Thank you for taking the course, and good luck as you continue to explore data science!

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.