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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Dr. Sanjay Agal, Ms. Krishna Raulji, Mr. Nikunj Bhavsar, Dr. Shiv Shakti Shrivastava
Dr. Sanjay Agal
Professor and Head,
Department of Artificial Intelligence and Data Science, Parul University, India
Dr. Sanjay Agal is a recognized academician, data scientist, and author with profound expertise in data science, machine learning, artificial intelligence, and data engineering. Holding a Ph.D. in Computer Science with a specialization in predictive analytics, Dr. Agal has made impactful contributions to both academia and industry through his research, publications, and real-world implementations of data-centric solutions.
With over 16 years of professional and academic experience, Dr. Agal has collaborated with renowned organizations, including multinational corporations, technology firms, and research institutes, contributing to the development of scalable machine learning models, data processing pipelines, and AI-driven systems. His scholarly work is reflected in numerous peer-reviewed publications in reputed journals and international conferences, and he is frequently invited as a keynote speaker at academic and industry forums focused on data science and AI.
As an educator, Dr. Agal is known for his ability to simplify complex data concepts through practical pedagogy. He has designed and delivered specialized courses in data wrangling, machine learning, AI, and statistics at premier academic institutions. His teaching philosophy emphasizes hands-on, application-driven learning, grounded in real-world challenges and aligned with best practices for ethical and responsible AI.
In this book, "Data Wrangling and Preprocessing," Dr. Agal distills years of experience into a comprehensive, accessible, and practical guide aimed at empowering readers with the essential skills required to transform raw data into meaningful, analysis-ready datasets. By blending theoretical foundations with hands-on techniques and Python-based implementations, this work offers invaluable guidance for anyone engaged in data science, from beginners to seasoned professionals.
As both the author and editor, Dr. Agal ensures that the content is technically sound, practically relevant, and pedagogically effective, positioning this book as a go-to reference for mastering data wrangling and preprocessing in Python.
