Table of Contents
Preface
Section 1: Getting Started with Statistics for Data Science
Chapter 1: Fundamentals of Data Collection, Cleaning, and Preprocessing
Technical requirements
Collecting data from various data sources
Reading data directly from files
Obtaining data from an API
Obtaining data from scratch
Data imputation
Preparing the dataset for imputation
Imputation with mean or median values
Imputation with the mode/most frequent value
Outlier removal
Data standardization – when and how
Examples involving the scikit-learn preprocessing module
Imputation
Standardization
Summary
Chapter 2: Essential Statistics for Data Assessment
Classifying numerical and categorical variables
Distinguishing between numerical and categorical variables ...
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