Hardcover. In a world awash with data, the real value lies in how well it's prepared. Data Wrangling and Preprocessing by Dr. Sanjay Agal, a seasoned academic and Head of Artificial Intelligence and Data Science at Parul University, is a definitive guide to transforming messy, raw data into high-quality, analysis-ready insights.Blending theoretical clarity with hands-on Python-based techniques, this book demystifies the most overlooked yet critical phase in the data science pipeline. From cleaning missing values and detecting outliers to advanced feature engineering and building automated data pipelines, readers are guided through practical, real-world challenges using tools like Pandas, NumPy, and Scikit-learn.Whether you're a student, educator, or industry professional, this book offers a structured, example-rich roadmap to mastering the art of data preparation-equipping you with the skills to ensure your models are built on rock-solid foundations. With a focus on reproducibility, quality, and efficiency, it's an essential companion for anyone serious about delivering reliable data-driven outcomes. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Bibliographic Details

Title: Data Wrangling and Preprocessing (Hardcover)
Publisher: Notion Press
Publication Date: 2025
Language: English
ISBN 13: 9798898547653
Binding: Hardcover
Condition: new

About this title

Synopsis

In a world awash with data, the real value lies in how well it's prepared. Data Wrangling and Preprocessing by Dr. Sanjay Agal, a seasoned academic and Head of Artificial Intelligence and Data Science at Parul University, is a definitive guide to transforming messy, raw data into high-quality, analysis-ready insights.

Blending theoretical clarity with hands-on Python-based techniques, this book demystifies the most overlooked yet critical phase in the data science pipeline. From cleaning missing values and detecting outliers to advanced feature engineering and building automated data pipelines, readers are guided through practical, real-world challenges using tools like Pandas, NumPy, and Scikit-learn.

Whether you're a student, educator, or industry professional, this book offers a structured, example-rich roadmap to mastering the art of data preparation—equipping you with the skills to ensure your models are built on rock-solid foundations. With a focus on reproducibility, quality, and efficiency, it’s an essential companion for anyone serious about delivering reliable data-driven outcomes.

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