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Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python,R,and command-line tools
by 作者: David Mertz
Publisher Finelybook 出版社: Packt Publishing (March 31,2021)
Language 语言: English
pages 页数: 498 pages
ISBN-10 书号: 1801071292
ISBN-13 书号: 9781801071291
Book Description
A comprehensive guide for data scientists to master effective data cleaning tools and techniques
It is something of a truism in data science,data analysis,or machine learning that most of the effort needed to achieve your actual purpose lies in cleaning your data. Written in David’s signature friendly and humorous style,this book discusses in detail the essential steps performed in every production data science or data analysis pipeline and prepares you for data visualization and modeling results.
The book dives into the practical application of tools and techniques needed for data ingestion,anomaly detection,value imputation,and feature engineering. It also offers long-form exercises at the end of each chapter to practice the skills acquired.
You will begin by 作者: looking at data ingestion of data formats such as JSON,CSV,SQL RDBMSes,HDF5,NoSQL databases,files in image formats,and binary serialized data structures. Further,the book provides numerous example data sets and data files,which are available for download and independent exploration.
Moving on from formats,you will impute missing values,detect unreliable data and statistical anomalies,and generate synthetic features that are necessary for successful data analysis and visualization goals.
by 作者: the end of this book,you will have acquired a firm understanding of the data cleaning process necessary to perform real-world data science and machine learning tasks.
What you will learn
Identify problem data pertaining to individual data points
Detect problem data in the systematic “shape” of the data
Remediate data integrity and hygiene problems
Prepare data for analytic and machine learning tasks
Impute values into missing or unreliable data
Generate synthetic features that are more amenable to data science,data analysis,or visualization goals.
