Cleaning Data for Effective Data Science
Data in its raw state is rarely ready for productive analysis. This book not only teaches you data preparation, but also what questions you should ask of your data. It focuses on the thought processes necessary for successful data cleaning as much as on concise and precise code examples that express these thoughts.
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Difficulty Level
Intermediate
Completion Time
16h36m
Language
English
About Book
Who Is This Book For?
This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.
Cleaning Data for Effective Data Science
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 16h36m total length
Data Ingestion – Tabular Formats
Data Ingestion - Hierarchical Formats
Data Ingestion - Repurposing Data Sources
The Vicissitudes of Error - Anomaly Detection
The Vicissitudes of Error - Data Quality
Rectification and Creation - Value Imputation
Rectification and Creation - Feature Engineering
Ancillary Matters - Closure/Glossary
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