Section 4 Required Texts

4.1 Course Notes

Professor Love maintains a set of Course Notes, titled Data Science for Biological, Medical and Health Research: Notes for 431. Professor Love revises the Notes every year, and so they appear in pieces as the semester progresses.

Although these Notes share some of the features of a textbook, they are neither comprehensive nor completely original. The main purpose is to give 431 students a set of common materials on which to draw during the course, providing a series of examples using R to work through issues that are likely to come up during the semester, and in later work.

In addition, slides and video recordings from each of Professor Love’s lectures, plus other in-class materials will be posted for your use throughout the semester.

Once class begins, you’ll be able to access all materials (including the Course Notes) through the main course website at https://thomaselove.github.io/431-2022/.

4.2 Buy This Book!

During the course, we will read David Spiegelhalter’s The Art of Statistics, which was first published by Penguin in March 2019 (and February 2020) in the UK and then by Basic Books in the US in September 2019. You can purchase any of the available versions (hard-cover, paperback or e-reader) online or in your local bookstore for about $20.

• Dr. Spiegelhalter’s website has lots of useful information.
• The book’s website contains R code, corrections and other materials.
• You are welcome to read this book before class starts, if you’d like to get a jump on things, but that’s not necessary: we’ll link readings to the Course Calendar.

4.3 Other Books to Download

There are three additional free books that you will definitely need to obtain during the semester and may be interested in looking at before class begins. Simply visit the links below.

1. Introduction to Modern Statistics by Mine Cetinkaya-Rundel and Johanna Hardin.
2. R for Data Science by Garrett Grolemund and Hadley Wickham (first edition). Note that the second edition (work in progress) may be better for our purposes, and is found at https://r4ds.hadley.nz/.
3. Biostatistics for Biomedical Research by Frank E. Harrell Jr. The related set of YouTube videos can be found here.

4.4 Key Articles and Posts

While I will recommend dozens, perhaps hundreds of articles, blog posts and the like to you over the course of the year, these are especially important in 431.

1. Several of the guides prepared by Jeff Leek and his group, including:
   • Finally, a Formula for Decoding Health News, from fivethirtyeight.com
   • Reading academic (scientific) papers,
   • Writing your first academic paper
   • Write papers like a modern scientist
   • How to Share Data for Collaboration by Shannon E. Ellis and Jeffrey T. Leek in The American Statistician, 2018 Special Issue on Data Science, or you can read the PeerJ preprint version here.
2. Data Organization in Spreadsheets by Karl W. Broman and Kara H. Woo in The American Statistician, 2018 Special Issue on Data Science, or you can read the PeerJ preprint version.
   • The Ellis/Leek and Broman/Woo papers are part of the Practical Data Science for Stats collection, which may be of interest.
3. Project-oriented workflow at tidyverse.org from Jenny Bryan.
4. From the Ten Simple Rules series at PLOS Computational Biology:
   • Ten Simple Rules for Effective Statistical Practice by Kass RE et al. 2016
   • Ten Simple Rules for Graduate Students by Gu J Bourne PE 2007
   • Ten Simple Rules for Better Figures by Rougier NP Droettboom M Bourne PE 2014
   • Ten Simple Rules for Creating a Good Data Management Plan by Michener WK 2015
5. Statistical Inference in the 21st Century: A World Beyond p < 0.05 from 2019 in The American Statistician
6. The American Statistical Association’s 2016 Statement on p-Values: Context, Process and Purpose.

Professor Love’s class-specific READMEs will provide links to these articles and other recommended readings as the semester goes on.