Mathematics

This book is designed to offer a fast-paced yet thorough introduction to essential statistical concepts using Python code samples, and aims to assist data scientists in their daily endeavors. The ability to extract meaningful insights from data requires a deep understanding of statistics. The book ensures that each topic is introduced with clarity, followed by executable Python code samples that can be modified and applied according to individual needs. Topics include working with data and exploratory analysis, the basics of probability, descriptive and inferential statistics and their applications, metrics for data analysis, probability distributions, hypothesis testing, and more. Appendices on Python and Pandas have been included. From foundational Python concepts to the intricacies of statistics, this book serves as a comprehensive resource for both beginners and seasoned professionals.

The book is designed to cover all major aspects of applied numerical methods, including numerical computations, solution of algebraic and transcendental equations, finite differences and interpolation, curve fitting, correlation and regression, numerical differentiation and integration, matrices and linear system of equations, numerical solution of ordinary differential equations, and numerical solution of partial differential equations. MATLAB is incorporated throughout the text and most of the problems are executed in MATLAB code. It uses a numerical problem-solving orientation with numerous examples, figures, and end of chapter exercises. Presentations are limited to very basic topics to serve as an introduction to more advanced topics.

Features:

• Integrates MATLAB throughout the text
• Includes over 600 fully-solved problems with step-by-step solutions
• Limits presentations to basic concepts of solving numerical methods

The book is designed to cover all major aspects of applied numerical methods, including numerical computations, solution of algebraic and transcendental equations, finite differences and interpolation, curve fitting, correlation and regression, numerical differentiation and integration, matrices and linear system of equations, numerical solution of ordinary differential equations, and numerical solution of partial differential equations. It uses a numerical problem-solving orientation with numerous examples, figures, and end of chapter exercises. Presentations are limited to very basic topics to serve as an introduction to more advanced topics.

This book provides over 1200 review questions, explanations, and answers for all types of engineering mathematics exams and review. It covers all the aspects of engineering topics from linear algebra and calculus to differential equations, complex analysis, statistics, graph theory, and more.

No detailed description available for "Algebra Essentials".

No detailed description available for "Optimization Using Linear Programming".

This book can be used in courses on mathematical modeling at the senior undergraduate or graduate level, or used as a reference for in-service scientists and engineers. The book provides an overview of mathematical modeling through a panoramic view of applications of mathematics in science and technology. In each chapter, mathematical models are chosen from the physical, biological, social, economic, management, and engineering sciences. The models deal with different concepts, but have a common mathematical structure and bring out the unifying influence of mathematical modeling in different disciplines.

Features:

• Provides a balance between theory and applications
• Features models from the physical, biological, social, economic, management, and engineering sciences

No detailed description available for "Foundations of Mathematics".