[new book recommendation] cleaning data for effective data science

Hello everyone , The purpose of this account is to share some of the world's latest technical books and information for programmers who want to improve themselves , Today brings with it 2021 year 3 Month by month Packt The latest book on big data published by the publishing house , The language involved is Python and R Language .

Cleaning Data for Effective Data Science

author ：David Mertz
Press. ：Packt
Publication date ：2021-03-31
ISBN：9781801071291

Book Introduction

In Data Science , Data analysis or machine learning , Most of the work required to achieve practical purposes is to clean up the data , This is self-evident . This book uses David The iconic friendly humorous style , The basic steps performed in each production data science or data analysis pipeline are discussed in detail , And it is ready for data visualization and modeling results .

This book delves into data extraction , Anomaly detection , Practical application of tools and technologies required for value estimation and functional engineering . Long exercises are provided at the end of each chapter , To practice the skills acquired .

You will first view such as JSON,CSV,SQL RDBMSes,HDF5,NoSQL database , Image format file and data capture of data format such as binary serialized data structure . Besides , This book provides many sample datasets and data files , Available for download and independent exploration .

Continue from format , You will estimate the missing value , Detect unreliable data and statistical anomalies , And generate the comprehensive functions necessary for successful data analysis and visualization .

By the end of the book , You will have an in-depth understanding of the data cleansing process required to perform actual data science and machine learning tasks .

What will you learn

• How to carefully consider your data and ask the right questions

• Identify problem data related to a single data point

• Based on the data of the system “ shape ” Detect problem data

• Remediate data integrity and health issues

• Prepare data for analysis and machine learning tasks

• Interpolate values into missing or unreliable data

• Generation is more suitable for data science , A comprehensive function of data analysis or visualization objectives .

Who is this book for

This book is designed to make software developers interested in data analysis or scientific computing , Data scientist , Aspiring data scientists and students benefit .

Familiar with statistical knowledge , General concepts of machine learning , programing language （Python or R） Knowledge of data science and some understanding of data science will be very helpful . glossary , Reference materials and friendly help should help all readers quickly master .

This text will also be helpful for intermediate and advanced data scientists who want to improve their data hygiene rigor and review data preparation issues .

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