• Sat. Feb 8th, 2025

Mathematical Modelling for Data Science

  • Home
  • Mathematical Modelling for Data Science

ISBN: 978-81-967685-0-8

Authors: Mrs. Shazia Tahseen, Dr. Indu Tyagi, Dr. R. Srilatha, Dr. Karthik Chinnasamy

Product Price ₹750.00
Click to add this item to cart.
Product Shipping
  • Shipping Cost (₹63.00)

Book Description

Mathematical Modeling for Data Science.” In the dynamic landscape of data science, the role of mathematical modeling is increasingly vital. As the volume and complexity of available data continue to grow, the ability to formulate, analyze, and interpret mathematical models becomes a key skill for practitioners and researchers alike.

This book is designed as a comprehensive guide for individuals entering the realm of data science or seeking to deepen their understanding of the mathematical foundations that underpin the discipline. It covers a wide spectrum of topics, from fundamental mathematical concepts to advanced modeling techniques, with a specific focus on their applications in the realm of data science.

Key Features:

  1. Foundational Concepts: The book begins with a solid foundation in mathematical concepts essential for data science, ensuring readers have a clear understanding of the tools at their disposal.
  2. Statistical Modeling: Explore statistical models and their applications in data science, from simple linear regression to more complex multivariate models. Understand the principles of hypothesis testing and statistical inference.
  3. Machine Learning Models: Dive into the world of machine learning with a focus on the mathematical principles behind popular algorithms. Gain insights into model evaluation, hyperparameter tuning, and ensemble methods.
  4. Optimization Techniques: Learn optimization methods crucial for fine-tuning models and enhancing their efficiency. Understand how to formulate and solve optimization problems in the context of data science applications.
  5. Time Series Analysis: Explore the intricacies of time-dependent data through the lens of mathematical modeling. Understand how to model and forecast time eries data, an essential skill for various data-driven industries.
  6. Case Studies and Practical Applications: Theoretical knowledge is complemented by real-world case studies and practical examples, providing readers with hands-on experience in applying mathematical models to diverse data science scenarios.

Target Audience:

This book is suitable for students, researchers, and professionals in data science, mathematics, computer science, and related fields. Whether you are just beginning your journey in data science or seeking to enhance your modeling skills, this book aims to be a valuable resource.

How to Use This Book:

Each chapter is designed to be self-contained, allowing readers to focus on specific topics of interest. However, for a comprehensive understanding, it is recommended to follow the chapters sequentially. Exercises and examples are provided to reinforce concepts and facilitate practical application.

Embark on this journey into the world of mathematical modeling for data science. We hope this book serves as a valuable companion, empowering you to tackle complex data challenges and extract meaningful insights from the vast sea of information.

<< Return to All Books