Foundations of Applied Mathematics for Engineering and Data Science

By Prajakta Shridhar Shingate and Akshay Dilip Changle

This book is authored by Akshay Dilip Changle is an Assistant Professor in the Engineering Science Department at Trinity Academy of Engineering College, Pune, with 7 years of teaching experience in helping students to learn and achieve academic success. He holds a postgraduate degree (M.Sc.) in Mathematics from S P College Pune under Savitribai Phule Pune University. Also qualified SET for eligibility for Assistant Professor in the subject Mathematical Sciences. Prof. Prajakta Shridhar Shingate holds a Master's degree in Mathematics (M.Sc.) and a Bachelor of Education (B.Ed.), and is currently pursuing a Ph.D. at Shivaji University, Kolhapur. With over 14 years of teaching experience, she brings deep academic insight and dedication to the field of mathematics education. Prof. Shingate is presently serving as a faculty member at the Navsahyadri Group of Institutions, College of Engineering, Naigaon, Pune, where she continues to contribute to both teaching and academic development. Her areas of interest include applied mathematics, mathematical modeling, and innovative teaching methodologies in engineering education. Sujata Nikhil Sabale is an enthusiastic and committed educator with 9 years of experience in teaching Mathematics. She holds an M.Sc. in Mathematics and a B.Ed., and is currently pursuing her Ph.D. at Shivaji University, Kolhapur. With a strong academic background and a passion for teaching, Prof. Sabale continues to contribute meaningfully to the academic community. This book serves as a comprehensive guide to the essential mathematical tools and techniques applied in modern engineering and data science. It covers a wide range of foundational topics including mathematical modeling, probability, statistics, linear algebra, and optimization techniques. The book offers in-depth explanations of how these concepts are used to model, analyze, and solve real-world problems, integrating practical examples from fields such as structural engineering, robotics, control systems, quality control, and data-driven decision-making. Special focus is given to computational methods, statistical tools, simulations, and AI-enhanced approaches that make complex systems manageable in the face of uncertainty and variability. Throughout its chapters, the book bridges the gap between theoretical concepts and practical applications, providing readers with a logical and structured framework for data analysis, process optimization, predictive modeling, and system evaluation. It emphasizes the importance of responsible, evidence-based decision-making in engineering and data science, especially in today's data-intensive, uncertainty-driven environments. By mastering these concepts, students, researchers, and professionals will gain the quantitative skills necessary for developing reliable, efficient, and intelligent solutions across diverse industries.

• Format: Paperback
• Language: English
• ISBN: B0F4X8SWZB
• ISBN13: 9798319432384
• Release Date: April 2025
• Publisher: Independently published
• Length: 106 Pages
• Weight: 0.76 lbs.
• Dimensions: 11.7" x 0.2" x 8.3"