
Advanced Mathematical Applications in Data Science
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Advanced Mathematical Applications in Data Science
About this book
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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Information
Table of contents
- Welcome
- Table of Content
- Title
- BENTHAM SCIENCE PUBLISHERS LTD.
- FOREWORD
- PREFACE
- List of Contributors
- The Role of Mathematics in Data Science: Methods, Algorithms, and Computer Programs
- Kalman Filter: Data Modelling and Prediction
- The Role of Mathematics and Statistics in the Field of Data Science and its Application
- Bag of Visual Words Model - A Mathematical Approach
- A Glance Review on Data Science and its Teaching: Challenges and Solutions
- Optimization of Various Costs in Inventory Management using Neural Networks
- Cyber Security in Data Science and its Applications
- Artificial Neural Networks for Data Processing: A Case Study of Image Classification
- Carbon Emission Assessment by Applying Clustering Technique to World’s Emission Datasets
- A Machine Learning Application to Predict Customer Churn: A Case in Indonesian Telecommunication Company
- A State-Wise Assessment of Greenhouse Gases Emission in India by Applying K-mean Clustering Technique
- Data Mining Techniques: New Avenues for Heart Disease Prediction
- Data Science and Healthcare