Cleaning Data For Effective Data Science Doing The Other 80 Of The Work With Python R And Commandline Tools Mertz by Mertz, David 9781801071291, 1801071292 instant download after payment.

A comprehensive guide for data scientists to master effective data cleaning tools and techniques Key Features: Master data cleaning techniques in a language-agnostic manner Learn from intriguing hands-on examples from numerous domains, such as biology, weather data, demographics, physics, time series, and image processing Work with detailed, commented, well-tested code samples in Python and R Book Description: 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 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 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