Advanced Mathematical Applications in Data Science

Advanced Mathematical Applications in Data Science comprehensively explores the crucial role mathematics plays in the field of data science. Each chapter is contributed by scientists, researchers, and academicians. The 13 chapters cover a range of mathematical concepts utilized in data science, enabling readers to understand the intricate connection between mathematics and data analysis. The book covers diverse topics, including, machine learning models, the Kalman filter, data modeling, artificial neural networks, clustering techniques, and more, showcasing the application of advanced mathematical tools for effective data processing and analysis. With a strong emphasis on real-world applications, the book offers a deeper understanding of the foundational principles behind data analysis and its numerous interdisciplinary applications. This reference is an invaluable resource for graduate students, researchers, academicians, and learners pursuing a research career in mathematical computing or completing advanced data science courses. Key Features: - Comprehensive coverage of advanced mathematical concepts and techniques in data science - Contributions from established scientists, researchers, and academicians - Real-world case studies and practical applications of mathematical methods - Focus on diverse areas, such as image classification, carbon emission assessment, customer churn prediction, and healthcare data analysis - In-depth exploration of data science's connection with mathematics, computer science, and artificial intelligence - Scholarly references for each chapter - Suitable for readers with high school-level mathematical knowledge, making it accessible to a broad audience in academia and industry.

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