Data Wrangling and Preprocessing

Sanjay Agal

ISBN 13: 9798898547646

Published by Notion Press Media Pvt. Ltd Jul 2025, 2025

About this Item

Neuware - 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.

Seller Inventory # 9798898547646

Bibliographic Details

• Title: Data Wrangling and Preprocessing
• Publisher: Notion Press Media Pvt. Ltd Jul 2025
• Publication Date: 2025
• Language: English
• ISBN 13: 9798898547646
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 17D millimeters 229 millimeters width by 152 millimeters height by 17 millimeters depth
• Weight: 476 grams

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.