Students in the Statistical Inference Course within the Johns Hopkins University Data Science Specialization on coursera.org are often interested in finding additional references to support the content delivered in the Statistical Inference Course.
| Reference | Description |
|---|---|
| Statistical Inference for Data Science | Start here! Written by Brian Caffo, the instructor for Statistical Inference, this is the book that is designed to accompany the Johns Hopkins University Statistical Inference course, and it's available for low (or no) cost on leanpub.org. |
| OpenIntro Statistics | Openintro.org provides a set of three statistics courses that include books and videos. The courses are targeted at different categories of students, ranging from high school to college. All of these resources are available for free. |
| ModernDive - An Introduction to the Statistical and Data Sciences via R | Developed by Chester Ismay and Albert Kim, ModernDive - An Introduction to the Statistical and Data Sciences via R is a gentle introduction to data science and statistics using R. For students who are frustrated by the hacker mentality in the Johns Hopkins Data Science Specialization, this online book will help students get through the Statistical Inference content. Chapter 8 on Hypothesis Testing is directly applicable to the JHU Statistical Inference course. |
| Free Statistics eBooks | R-Statistics blog article by Tal Galili that contains links to a number of free eBooks on statistics from a variety of sources. Make sure you review the comments, because they include additional sources of content provided by readers of the original blog post. |
| Introduction to Statistical Thought | Written by Michael Lavine at the University of Massachusetts, this book provides an introduction to statistics for people who are knowledgeable in calculus. The book is available for free on the University of Massachusetts website and may be accessed by clicking on the book title hyperlink to the left of this description. |
| R in Action | Written by Robert Kabacoff, R in Action is an excellent overall reference for R, written by a statistician. It includes code and detailed explanations of a variety of techniques used in the Statistical Inference course, such as basic statistics, t-tests, comparing multiple groups, and power analysis. |
| Statmethods.net Basic Statistics Page | Also developed by Robert Kabacoff, statmethods.net is the free online companion to R in Action. It contains content about a variety of statistical methods, as well as a list of additional books and tutorials. |
| Wikipedia | Wikipedia is always a great place to start when one needs to answer some questions about a statistical method. For example, the link on the Central Limit Theorem is useful for one of the two components of the Statistical Inference course project. |
| An Introduction to Statistical Learning with Applications in R | Written by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, ISLR is the book to read before taking on Elements of Statistical Learning. The first printing of the book is available for free on the University of Southern California website and may be accessed by clicking on the book title hyperlink to the left of this description. |
| The Elements of Statistical Learning | ESL is very useful as a supplement to the Practical Machine Learning course later in the Data Science Specialization. Note that it is not an appropriate reference for Statistical Inference. |
| Schaum's Outline of Statistics | Having relied on Schaum's Outlines to get me through engineering calculus when I was in college, I've always appreciated the problem solving approach in the Schaum's books. The Statistics Outline (Fifth Edition) includes more than 500 fully solved problems, examples, and practice exercises. it also includes access to detailed videos that are related to the problems. |
| Schaum's Outline of Probability, Random Variables and Processes | The Probability, Random Variables, and Processes Outline (Third Edition) is helpful for the Bayesian probability problems discussed in the Statistical Inference course. The Outline includes 405 solved problems and 23 online videos. |
We will continue to periodically update this list with new reference material.
Last updated: 2 April 2018