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Cleaning Data for Effective Data Science

Doing the other 80% of the work with Python, R, and command-line tools

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 DescriptionData cleaning is the all-important first step to successful data science, data analysis, and machine learning. Les mer
Vår pris
466,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager

Paperback
Legg i
Paperback
Legg i
Vår pris: 466,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager

Om boka

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 DescriptionData 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 forThis 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.

Fakta

Innholdsfortegnelse

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

Om forfatteren

David Mertz, Ph.D. is the founder of KDM Training, a partnership dedicated to educating developers and data scientists in machine learning and scientific computing. He created a data science training program for Anaconda Inc. and was a senior trainer for them. With the advent of deep neural networks, he has turned to training our robot overlords as well. He previously worked for 8 years with D. E. Shaw Research and was also a Director of the Python Software Foundation for 6 years. David remains co-chair of its Trademarks Committee and Scientific Python Working Group. His columns, Charming Python and XML Matters, were once the most widely read articles in the Python world.