Cleaning Data for Effective Data Science: Doing the other 80% of the work with Python, R, and command-line tools. Code

Code Think about your data intelligently and ask the right questions Key Features Master data cleaning techniques necessary to perform real-world data science and machine learning tasks Spot common problems with dirty data and develop flexible solutions from first principles Test and refine your newly acquired skills through detailed exercises at the end of each chapter Book Description Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses. What you will learn Ingest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structures Understand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and Bash Apply useful rules and heuristics for assessing data quality and detecting bias, like Benford's law and the 68-95-99.7 rule Identify and handle unreliable data and outliers, examining z-score and other statistical properties Impute sensible values into missing data and use sampling to fix imbalances Use dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your data Work carefully with time series data, performing de-trending and interpolation Who this book is 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. Table of Contents 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 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 How to think carefully about your data and ask the right questions 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. Who this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, and students who are interested in data analysis or scientific computing. Basic familiarity with statistics, general concepts in machine learning,..