Practical Statistics for Data Scientists

50 Essential Concepts

Peter Bruce, Andrew Bruce (Autoren)

Buch | Softcover

320 Seiten

2017
O'Reilly Media (Verlag)
978-1-4919-5296-2 (ISBN)

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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

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.

Chapter 1 Exploratory Data Analysis
Chapter 2 Data and Sampling Distributions
Chapter 3 Statistical Experiments and Significance Testing
Chapter 4 Regression and Prediction
Chapter 5 Classification
Chapter 6 Statistical Machine Learning
Chapter 7 Unsupervised Learning

Erscheinungsdatum	02.06.2017
Verlagsort	Sebastopol
Sprache	englisch
Maße	179 x 233 mm
Gewicht	510 g
Einbandart	kartoniert
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
	Mathematik / Informatik ► Informatik ► Theorie / Studium
	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
	Mathematik / Informatik ► Mathematik ► Statistik
Schlagworte	Data Science • Datenanalyse • Mathematik • Programmiersprache R • Statistik • Statistische Anwendungen • Statistische Konzepte
ISBN-10	1-4919-5296-2 / 1491952962
ISBN-13	978-1-4919-5296-2 / 9781491952962
Zustand	Neuware