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Roy Jafari - Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics

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Roy Jafari Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics
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Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics: summary, description and annotation

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This book will make the link between data cleaning and preprocessing to help you design effective data analytic solutions

Key Features
  • Develop the skills to perform data cleaning, data integration, data reduction, and data transformation
  • Get ready to make the most of your data with powerful data transformation and massaging techniques
  • Perform thorough data cleaning, such as dealing with missing values and outliers
Book Description

Data preprocessing is the first step in data visualization, data analytics, and machine learning, where data is prepared for analytics functions to get the best possible insights. Around 90% of the time spent on data analytics, data visualization, and machine learning projects is dedicated to performing data preprocessing.

This book will equip you with the optimum data preprocessing techniques from multiple perspectives. Youll learn about different technical and analytical aspects of data preprocessing data collection, data cleaning, data integration, data reduction, and data transformation and get to grips with implementing them using the open source Python programming environment. This book will provide a comprehensive articulation of data preprocessing, its whys and hows, and help you identify opportunities where data analytics could lead to more effective decision making. It also demonstrates the role of data management systems and technologies for effective analytics and how to use APIs to pull data.

By the end of this Python data preprocessing book, youll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques; and handle outliers or missing values to effectively prepare data for analytic tools.

What you will learn
  • Use Python to perform analytics functions on your data
  • Understand the role of databases and how to effectively pull data from databases
  • Perform data preprocessing steps defined by your analytics goals
  • Recognize and resolve data integration challenges
  • Identify the need for data reduction and execute it
  • Detect opportunities to improve analytics with data transformation
Who this book is for

Junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data will find this book useful. Basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are assumed.

Table of Contents
  1. Review of the Core Modules of NumPy and Pandas
  2. Review of Another Core Module - Matplotlib
  3. Data What Is It Really?
  4. Databases
  5. Data Visualization
  6. Prediction
  7. Classification
  8. Clustering Analysis
  9. Data Cleaning Level I - Cleaning Up the Table
  10. Data Cleaning Level II - Unpacking, Restructuring, and Reformulating the Table
  11. Data Cleaning Level III- Missing Values, Outliers, and Errors
  12. Data Fusion and Data Integration
  13. Data Reduction
  14. Data Transformation and Massaging
  15. Case Study 1 - Mental Health in Tech
  16. Case Study 2 - Predicting COVID-19 Hospitalizations
  17. Case Study 3: United States Counties Clustering Analysis
  18. Summary, Practice Case Studies, and Conclusions

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Hands-On Data Preprocessing in Python Learn how to effectively prepare data for - photo 1
Hands-On Data Preprocessing in Python

Learn how to effectively prepare data for successful data analytics

Roy Jafari

BIRMINGHAMMUMBAI Hands-On Data Preprocessing in Python Copyright 2022 Packt - photo 2

BIRMINGHAMMUMBAI

Hands-On Data Preprocessing in Python

Copyright 2022 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Group Product Manager: Gebin George

Publishing Product Manager: Ali Abidi

Senior Editor: Roshan Kumar

Content Development Editor: Priyanka Soam

Technical Editor: Sonam Pandey

Copy Editor: Safis Editing

Project Coordinator: Aparna Ravikumar Nair

Proofreader: Safis Editing

Indexer: Pratik Shirodkar

Production Designer: Nilesh Mohite

Marketing Coordinator: Shifa Ansari

First published: January 2022

Production reference: 1161221

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham

B3 2PB, UK.

978-1-80107-213-7

www.packt.com

To my parents Soqra Bayati and Jahanfar Jafari Contributors About the author - photo 3

To my parents,

Soqra Bayati

and

Jahanfar Jafari.

Contributors
About the author

Roy Jafari, Ph.D. is an assistant professor of business analytics at the University of Redlands.

Roy has taught and developed college-level courses that cover data cleaning, decision making, data science, machine learning, and optimization.

Roy's style of teaching is hands-on and he believes the best way to learn is to learn by doing. He uses active learning teaching philosophy and readers will get to experience active learning in this book.

Roy believes that successful data preprocessing only happens when you are equipped with the most efficient tools, have an appropriate understanding of data analytic goals, are aware of data preprocessing steps, and can compare a variety of methods. This belief has shaped the structure of this book.

About the reviewers

Arsia Takeh is a director of data science at a healthcare company and is responsible for designing algorithms for cutting-edge applications in healthcare. He has over a decade of experience in academia and industry delivering data-driven products. His work involves the research and development of large-scale solutions based on machine learning, deep learning, and generative models for healthcare-related use cases. In his previous role as a co-founder of a digital health start-up, he was responsible for building the first integrated -omics platform that provided a 360 view of the user as well as personalized recommendations to improve chronic diseases.

Sreeraj Chundayil is a software developer with more than 10 years of experience. He is an expert in C, C++, Python, and Bash. He has a B.Tech from the prestigious National Institute of Technology Durgapur in electronics and communication engineering. He likes reading technical books, watching technical videos, and contributing to open source projects. Previously, he was involved in the development of NX, 3D modeling software, at Siemens PLM. He is currently working at Siemens EDA (Mentor Graphics) and is involved in the development of integrated chip verification software.

I would like to thank the C++ and Python communities who have made an immense contribution to molding me into the tech lover I am today.

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