Practical Statistics for Data Scientists: 50 Essential Concepts

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"O'Reilly Media, Inc.", May 10, 2017 - Computers - 318 pages
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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
 

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User Review  - encephalical - LibraryThing

For context, I'm not a data scientist, but a visualization programmer who works with some and was looking for a book to get acquainted with some background material. The first four chapters on ... Read full review

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Contents

Section 1
Section 2
Section 3
Section 4
Section 5
Section 6
Section 7
Section 8
Section 12
Section 13
Section 14
Section 15
Section 16
Section 17
Section 18
Section 19

Section 9
Section 10
Section 11
Section 20
Copyright

Other editions - View all

Practical Statistics for Data Scientists: 50 Essential Concepts
Peter Bruce,Andrew Bruce
Limited preview - 2017

Common terms and phrases

A/B Testing binary binomial BldgGrade boosting bootstrap boxplot categorical data categorical variables Chi-Square Test classification coefficient compute confidence interval correlation covariance matrix Cross-Validation data science data scientists data set default degrees of freedom discriminant analysis F-statistic Factor Variables Figure function Further Reading groups hierarchical clustering histogram Hot Encoder Hyperparameters hypothesis test K-means K-Nearest Neighbors Key Ideas Key Terms linear regression loan data logistic regression machine learning mean measure median method Multi-Arm Bandit multiple naive Bayes normal distribution number of clusters outcome outliers overfitting p-value parameters partial residual partition payment_inc_ratio Percentiles permutation test plot Polynomial population predicted values Prediction Intervals predictor variables principal components probability random forest Random Sampling records regression equation resampling sample statistic sampling distribution spline SqFtLot SqFtTotLiving standard deviation standard error Standardization Normalization statistically significant statisticians Synonyms t-distribution treatment tree models typically Unsupervised Learning variance versus Weibull distribution weights XGBoost Z-Scores

About the author (2017)

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.

Bibliographic information

QR code for Practical Statistics for Data Scientists
TitlePractical Statistics for Data Scientists: 50 Essential Concepts
AuthorsPeter Bruce, Andrew Bruce
Publisher"O'Reilly Media, Inc.", 2017
ISBN1491952911, 9781491952917
Length318 pages
Subjects ›  › 

Computers / Data Science / Data Warehousing
Computers / Data Science / General
Computers / Database Administration & Management
  
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