LibGuides: TIM-8521: Module 3

Lesson 6 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.

An Introduction to Bootstrap Methods with Applications to R
Chernick, M. R., & LaBudde, R. A. (2011). An Introduction to Bootstrap Methods with Applications to R. John Wiley & Sons.

Read Chapter 1: Introduction
Read Chapters 2.1 and 2.2
Read Chapters 3.1 and 3.2
Pattern Recognition and Machine Learning
Bishop, C. M. (2016). Pattern recognition and machine learning.
Read Chapter 14 ( Sections 14.2 and 14.3 )
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 Section7.3 Regularization for Deep Learning
Read Section 3.12 Dataset Augmentation

VIT: Visual Inference Tools
VIT is a developing collection of software modules for use in teaching and learning experiences aimed at developing the core concepts of statistical inference. VIT is desktop software.
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)
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/