DDS8501

Zybook Short Tutorial 

Students to learn how to access Zybook and Zylabs from within the Module. 

Data Science Foundations with R, Zylabs.com 

• Read Chapter 1: Introduction to Data Science 

• Read Chapter 2.  R for Data Science 

Zylabs reading provides the necessary foundation for data analysis environment setup. 

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

• Read Chapter 15.1 What is Data 

• Read Chapter 16.3 Data frames 

Zylabs reading provides the necessary foundation for data analysis environment setup. 

Analytical Environment Setup (Please choose at least one method): 

• How to use R in Google Colab? (2023, October 3). https://www.codingthepast.com/2023/10/03/R-in-GoogleColab.html 
• RStudio IDE User Guide – RStudio User Guide. (n.d.). RStudio User Guide. https://docs.posit.co/ide/user/ 
• Posit Cloud Documentation - Code Projects. (n.d.). https://docs.posit.co/cloud/guide/projects.html 

Students can use either RStudio, Google Colab, or Posit Cloud to perform R programming in this class.   

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. 

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 our work in R Markdown and publish it to RPubs (free). These skills are necessary in building a data science portfolio. 

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. 

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. 

Example of Executive Summary Framework 

Example resource to be used in the assignment.