Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, you’ll learn:
Why exploratory data analysis is a key preliminary step in data science
How random sampling can reduce bias and yield a higher quality dataset, even with big data
How the principles of experimental design yield definitive answers to questions
How to use regression to estimate outcomes and detect anomalies
Key classification techniques for predicting which categories a record belongs to
Statistical machine learning methods that “learn” from data
Unsupervised learning methods for extracting meaning from unlabeled data
Chapter 1Exploratory Data Analysis
Elements of Structured Data
Estimates of Location
Estimates of Variability
Exploring the Data Distribution
Exploring Binary and Categorical Data
Exploring Two or More Variables
Chapter 2Data and Sampling Distributions
Random Sampling and Sample Bias
Sampling Distribution of a Statistic
Poisson and Related Distributions
Chapter 3Statistical Experiments and Significance Testing
Peter Bruce founded and grew the Institute for Statistics Education at Statistics.com, which now offers about 100 courses in statistics, roughly a third of which are aimed at the data scientist. In recruiting top authors as instructors and forging a marketing strategy to reach professional data scientists, Peter has developed both a broad view of the target market, and his own expertise to reach it.
Andrew Bruce has over 30 years of experience in statistics and data science in academia, government and business. He has a Ph.D. in statistics from the University of Washington and published numerous papers in refereed journals. He has developed statistical-based solutions to a wide range of problems faced by a variety of industries, from established financial firms to internet startups, and offers a deep understanding the practice of data science.
The animal on the cover of Practical Statistics for Data Scientists is a lined shore crab (Pachygrapsus crassipes), also known as a striped shore crab. It is found along the coasts and beaches of the Pacific Ocean in North America, Central America, Korea, and Japan. These crustaceans live under rocks, in tidepools, and within crevices. They spend about half their time on land, and periodically return to the water to wet their gills.
The lined shore crab is named for the green stripes on its brown-black carapace. It has red claws and purple legs, which also have a striped or mottled pattern. The crab generally grows to be 3–5 centimeters in size; females are slightly smaller. Their eyes are on flexible stalks that can rotate to give them a full field of vision as they walk.
Crabs are omnivores, feeding primarily on algae, but also mollusks, worms, fungi, dead animals, and other crustaceans (depending on what is available). They moult many times as they grow to adulthood, taking in water to expand and crack open their old shell. Once this is achieved, they spend several difficult hours getting free, and then must hide until the new shell hardens.
Many of the animals on O'Reilly covers are endangered; all of them are important to the world. To learn more about how you can help, go to animals.oreilly.com.
The cover image is from Pictorial Museum of Animated Nature.