LibGuides: DDS-8515 V2: Module 1

Lesson 1 Resources

Machine Learning with R
Lantz, B. (2023). Machine learning with R (4th ed.). Packt Publishing.
Read the following:
- Chapter 1: Introducing Machine Learning
- Chapter 2: Managing and Understanding Data
Chapter 1 provides introductory information on machine learning and Chapter 2 provides foundational knowledge to perform data exploration in R.

Learning R Programming. Become an Efficient Data Scientist With R
Run, K. (2016). Learning R programming. Become an efficient data scientist with R. Packt Publishing.

Google Colab
Colab. (n.d.). Welcome to Colab. Google Colab.
Set up a Google account to use this open-source R & Python platform to support multivariate analysis projects. The Getting Started section under the Table of Contents is helpful to those new to the environment. Note: You can skip the Gemini API setup section.

Machine Learning with R
Lantz, B. (2023). Machine learning with R (4th ed.). Packt Publishing.
Read the following:
- Chapter 13: Challenging Data – Too Much, Too Little, Too Complex

Applied Multivariate Statistics in R
Bakker, J. D. (n.d.). Applied multivariate statistics in R. University of Washington.
Read the following:
- Chapter 37: PCA
STAT 505 Applied Multivariate Statistical Analysis
Penn State Eberly College of Science (n.d.). STAT 505 Multivariate statistical analysis [MOOC]. Penn State University.
Read the following:
- Lesson 11: Principal Components Analysis (PCA)
The Connection Between Eigenvectors and Grids
Khanna, A. (2021, November 24). The connection between eigenvectors and grids. How to visualize eigenvectors and eigenvectors using geometry. Medium.