Practical Statistics for Data Scientists: 50 Essential ConceptsStatistical 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:
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Contents
Section 1 | |
Section 2 | |
Section 3 | |
Section 4 | |
Section 5 | |
Section 6 | |
Section 7 | |
Section 8 | |
Section 16 | |
Section 17 | |
Section 18 | |
Section 19 | |
Section 20 | |
Section 21 | |
Section 22 | |
Section 23 | |
Section 9 | |
Section 10 | |
Section 11 | |
Section 12 | |
Section 13 | |
Section 14 | |
Section 15 | |
Section 24 | |
Section 25 | |
Section 26 | |
Section 27 | |
Section 28 | |
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Practical Statistics for Data Scientists: 50 Essential Concepts Peter Bruce,Andrew Bruce Limited preview - 2017 |
Common terms and phrases
A/B test Bathrooms bias binary binomial BldgGrade boosting bootstrap boxplot categorical data categorical variables classification coefficient compute confidence interval correlation covariance matrix Cross-Validation data science data scientists data set default degrees of freedom discriminant analysis Exploratory Data 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 partition payment_inc_ratio percentile permutation test plot population predicted values predictor variables principal components probability random forest records regression equation resampling sample statistic Selecting the Number Selection and Stepwise spline SqFtLot SqFtTotLiving standard deviation standard error Standardization Normalization statistically significant statisticians Stepwise Regression Synonyms t-distribution treatment tree models typically variance versus weights XGBoost Z-Scores