LibGuides: TIM-8501 v5: Lesson 1 Resources

Lesson 1 Resources

Applied Statistics with Data Analytics (R), Zylabs.com

Read 1.1. What is Data?
Read 2.1 What is Data Visualization?
Read 2.2 R for Data Visualization.
Read 2.3 Data Frames

This reading details Steven’s typologies and instructs how to find the structure of a data set in R.

Data Science Foundations with R, Zylabs.com

Read Chapter 2. R for Data Science

This reading provides the necessary foundation for setting up the EDA environment.

ACCESS your Zylabs.com material from the Lesson module in your course.

RStudio: Integrated Development Environment for R
PositTeam. (2023). RStudio: Integrated development environment for R. Posit Software, PBC. Retrieved from http://www.posit.co/
The R Project for Statistical Computing
R Core Team. (2023). R: A language and environment for statistical computing. Retrieved from https://www.R-project.org/
Hands-on Exploratory Data Analysis with R: Become an Expert in Exploratory Data Analysis using R Packages
Datar, R., & Garg, H. (2019). Hands-on exploratory data analysis with R: Become an expert in exploratory data analysis using R packages. O'Reilly Media, Inc.
- Read Chapter 1. Setting Up Our Data Analysis Environment.
- Read Chapter 2. Importing Diverse Datasets
Helps students prepare their analytical environment.
R Programming for Statistics and Data Science
R Programming for Statistics and Data Science (Media from Packt Publishing available freely through O’Reilly Media Inc.). (2018).
- Watch and Read Chapter 1 through Chapter 3.
Helps students prepare their analytical environment.
NIST/SEMATECH Engineering Statistics Handbook
Smeaton, A. (2003). NIST/SEMATECH Engineering Statistics Handbook. https://www.itl.nist.gov/div898/handbook/
- Read Chapter 1.1. Exploratory Data Analysis Introduction
Smeaton defines EDA, its purpose, and its history, setting the stage for the entire course content.
R Markdown: The Definitive Guide
Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R markdown: The definitive guide. CRC Press. https://bookdown.org/yihui/rmarkdown/
- Read Chapter 1. Installation
- Read Chapter 2. Basics
This reading demonstrates how to compile your work in R Markdown and publish it to RPubs (free). These skills are necessary in building a data science portfolio.

Example of Executive Summary Framework

Optional Resources

Nominal, Ordinal, Interval, and Ratio Typologies are Misleading
Velleman, P. F., & Wilkinson, L. (1993). Nominal, ordinal, interval, and ratio typologies are misleading. The American Statistician, 47(1), 65-72.
This article provides a critical look at Steven’s typologies.