Introduction to DataScience  
Published by Vijay Nicole Imprints Private Limited
Publication Date:  Available in all formats
ISBN: 9789349825413
Pages: 346

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ISBN: 9789349825413 Price: INR 350.00
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This book, Introduction to Data Science, is a comprehensive textbook designed for undergraduate
students pursuing Computer Science. Combining theoretical knowledge with practical applications,
this book equips readers with essential skills to collect, process, analyze, and interpret data for informed decision-making.

Salient Features:

Covers the entire Data Science Lifecycle, from data collection and pre-processing to
modeling, evaluation, and deployment.
 Introduces key tools like Python, R, SQL, Jupyter Notebooks, and Google Colab, along with
techniques for data wrangling, visualization, and machine learning.
 Explores crucial concepts such as overtting, bias-variance tradeo, hyperparameter tuning,
and regularization techniques to improve model performance.
 Highlights AI fairness, bias mitigation, data privacy laws, and security threats in AI systems,
ensuring responsible data science practices.
 Includes case studies and examples to bridge the gap between theory and practice.
 Includes review questions, MCQs, and exercises to enhance understanding and reinforce learning

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This book, Introduction to Data Science, is a comprehensive textbook designed for undergraduate
students pursuing Computer Science. Combining theoretical knowledge with practical applications,
this book equips readers with essential skills to collect, process, analyze, and interpret data for informed decision-making.

Salient Features:

Covers the entire Data Science Lifecycle, from data collection and pre-processing to
modeling, evaluation, and deployment.
 Introduces key tools like Python, R, SQL, Jupyter Notebooks, and Google Colab, along with
techniques for data wrangling, visualization, and machine learning.
 Explores crucial concepts such as overtting, bias-variance tradeo, hyperparameter tuning,
and regularization techniques to improve model performance.
 Highlights AI fairness, bias mitigation, data privacy laws, and security threats in AI systems,
ensuring responsible data science practices.
 Includes case studies and examples to bridge the gap between theory and practice.
 Includes review questions, MCQs, and exercises to enhance understanding and reinforce learning

For Authors

Dr. C. Muthu is currently Head, Department of Data Science, Loyola College, Chennai, Tamil Nadu. An
experienced computer professional of over 38 years. Dr. C. Muthu has been teaching Python, Machine
Learning for 9 years. A prolic writer, his books include Programming with Java, Visual C#. Net and Basic.Net.
Dr. T. Rajaretnam is currently Head, Department of Data Science, St. Joseph's College, Trichy, Tamil Nadu.An experienced computer professional with over 36 years of experience, Dr. T. Rajaretnam has been teaching Python, Machine Learning, and Compiler Design for several years. He is also a passionate researcher and has published numerous research papers in reputed journals.
Mr. M. C. Prakash is currently providing consultancy services for Data Science projects at Shalom Infotech. He is an alumnus of elite institutions such as CEG and BIM. An IT Professional with 7 years of work experience in well known MNCs such as IBM and Cognizant, he is also a passionate researcher who has published five research papers in Analytics domain.

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