English | 2022 | ISBN: 9781801072137 | 251 pages | True EPUB | 24.25 MB

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 spent on data analytics, data visualization, and machine learning projects is dedicated to perfog data preprocessing.
This book will equip you with the optimum data preprocessing techniques from multiple perspectives. 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. 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, 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
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 will find this book useful. Basic programming skills, such as working with variables, conditionals, and loops, along with bner-level knowledge of Python and simple analytics experience, are assumed.
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



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