English | 2022 | ISBN: ‎ 1801072132 | 602 pages | True PDF EPUB | 87.33 MB

Get your raw data cleaned up and ready for processing to design better data analytic solutions

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 pr 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, eeering 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 bner-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