Hands-On Data Preprocessing in Python

eBook Details:

• Paperback: 602 pages
• Publisher: WOW! eBook (January 21, 2022)
• Language: English
• ISBN-10: 1801072132
• ISBN-13: 978-1801072137

eBook Description:

Hands-On Data Preprocessing in Python: This book will make the link between data cleaning and preprocessing to help you design effective data analytic solutions

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 Hands-On Data Preprocessing in Python 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.

• 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

By the end of this Hands-On Data Preprocessing in Python 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.

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