Download this PDF book: Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics by Roy Jafari
Key Features
Develop the skills to perform data cleaning, data integration, data reduction, and data transformation
Make the most of your raw data with powerful data transformation and massaging techniques
Perform thorough data cleaning, including dealing with missing values and outliers
Book Description
Hands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who's developed college-level courses on data preprocessing and related subjects.
With this book, you'll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data.
You'll 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.
The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making.
As you progress through the chapters, you'll also understand 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, you'll 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
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. You don't need any prior experience with data preprocessing to get started with this book.
However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.
Table of Contents
Review of the Core Modules of NumPy and Pandas
Review of Another Core Module - Matplotlib
Data – What Is It Really?
Databases
Data Visualization
Prediction
Classification
Clustering Analysis
Data Cleaning Level I - Cleaning Up the Table
Data Cleaning Level II - Unpacking, Restructuring, and Reformulating the Table
Data Cleaning Level III- Missing Values, Outliers, and Errors
Data Fusion and Data Integration
Data Reduction
Data Transformation and Massaging
Case Study 1 - Mental Health in Tech
Case Study 2 - Predicting COVID-19 Hospitalizations
Case Study 3: United States Counties Clustering Analysis
Summary, Practice Case Studies, and Conclusions
From the Author
What are the unique features of this book?
The book is a mesh between a college textbook and a technical step-by-step know-how book. The book covers both theories and the tools of data preprocessing.
The book's approach in preparing data for analysis is comprehensive, it sees data cleaning as only one part of data preprocessing. Data Integration, Data Reduction, Data Transformation are also covered along with data cleaning.
Unlike other data cleaning books, this book does not assume data cleaning can be done in isolation of the analytic situation but data preprocessing should be informed by the analytic goals and for fulfilling the objectives of the analytics.
The book introduces all the tools and techniques in the context of real analytic examples. This empowers the readers to be able to choose the right techniques in preparing data for their analytic situations.
The book provides meaningful and challenging experiences at the end of each chapter and also chapter 18 has 10 possible analytic projects that readers can take on to add to their data science portfolio.
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 book:
Publisher : Packt Publishing (January 21, 2022)
Language : English
Pages : 602
File : PDF, 19MB
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