LibGuides: TIM-8521: Module 2

Lesson 3 Required Resources

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

Data Science Foundations with R, Zylabs.com

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

Business Statistics: For Contemporary Decision Making, 7th Edition
Black K. (2011). Business statistics: contemporary decision making. John Wiley.
Read Unit II Continuous Distributions and Sampling Distributions (Chapter 6)
Introduction to Probability
Blitzstein, J. K., & Hwang, J. (2015). Introduction to probability. Crc Press.
Read Chapters 5: Continuous Random Variables
Read Chapter 6: Moments
Read Chapter 10: Inequalities and Limit Theorems
Pattern Recognition and Machine Learning
Bishop, C. M. (2016). Pattern recognition and machine learning.
Read Chapter 1. Introduction ( Section 1.2.4-1.2.6, 1.3 and 1.4)
Read Chapter 2. Probability Distributions ( Section 2.3 )
Deep Learning
Heaton, J. (2018). Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning: The MIT Press, 2016, 800 pp, ISBN: 0262035618. Genetic Programming and Evolvable Machines, 19(1–2), 305–307. https://doi.org/10.1007/s10710-017-9314-z
Read Section 3.9 Common Probability Distributions
Read Section 3.12 Technical Details of Continuous Variables

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

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

Business Statistics: For Contemporary Decision Making, 7th Edition
Black K. (2011). Business statistics: contemporary decision making. John Wiley.
Read Unit III Making Inferences about Population Parameters
Chapter 8: Statistical Inference: Estimation for Single Populations
Chapter 9: Statistical Inference: Hypothesis Testing for Single Populations
Chapter 10: Statistical Inference about two Populations
Pattern Recognition and Machine Learning
Bishop, C. M. (2016). Pattern recognition and machine learning.
Read Chapter 1. Introduction ( Section 1.7 )

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

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

Business Statistics: For Contemporary Decision Making, 7th Edition
Black K. (2011). Business statistics: contemporary decision making. John Wiley.
Read Unit V Non Parametric Statistics and Quality
Read Chapter 16 Analysis of Categorical Data
Read Chapter 17 Nonparametric Statistics
Read Chapter 18 Statistical Quality Control
Pattern Recognition and Machine Learning
Bishop, C. M. (2016). Pattern recognition and machine learning.
Read Chapter 4 Linear Models for Classification (section 4.1)

The Theory of Probability
Vogel, M. (2013). The Theory of Probability, by Santosh S. Venkatesh: Scope: text book. Level: advanced undergraduate.
Probability Approximations and Beyond
Barbour, Andrew., Chan, H. Peng., & Siegmund, David. (Eds.). (2012). Probability Approximations and Beyond (1st ed. 2012.). Springer New York. https://doi.org/10.1007/978-1-4614-1966-2
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/
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 10 ( Video on Distributions) Smeaton defines EDA, its purpose, and its history, setting the stage for the entire course content.
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
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/