Advanced Methods in Mathematics and Data Science
Concepts and Applications
- 1st Edition - February 1, 2027
- Latest edition
- Editors: Mehmet Yavuz, Fuat Usta, Ceylan Yozgatlıgil
- Language: English
Advanced Methods in Mathematics and Data Science: Concepts and Applications provides a comprehensive guide to topics in which mathematical modelling and applications play a pivota… Read more
Description
Description
Advanced Methods in Mathematics and Data Science: Concepts and Applications provides a comprehensive guide to topics in which mathematical modelling and applications play a pivotal role, including computational models in physics and chemistry, statistical models in life science, data analysis in engineering, as well as finance and social science applications. The book provides a clear and step-by-step presentation of advanced mathematical methods at the intersection of machine learning, artificial intelligence, big data analytics, and statistics, including control theory, topology, and nonlinear dynamic systems. The book progresses from concepts and foundational methods in the early chapters, to inter-disciplinary applications in computational mathematics, mathematical biology, fractional calculus, nonlinear dynamical systems, and data-driven optimization. The methods used to analyze data using a particular computational model are thoroughly explained through real-world and publicly available data sets. Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues, as well as definitions of key terms. The book helps readers enhance their conceptual understanding and practical application of computational methods to real-world data sets, and enables readers to gain competency at the intersection of advanced mathematics and AI, which is an important skill in today’s data-driven market.
Key features
Key features
- Presents the concepts of computational mathematics, control theory, topology, fractional calculus, and nonlinear dynamical systems in conjunction with data science methods such as AI, Machine Learning, computational statistics, big data analytics, optimization, and data-driven decision making
- Provides readers with conceptual understanding and practical application of computational models to real-world data sets, along with methods for the worked examples
- Addresses topics where mathematical modelling and applications play a pivotal role, including computational models in physics and chemistry, statistical models in life science, analysis in science and engineering, as well as finance and social science methods
Readership
Readership
Computer Science researchers, data science researchers, and data analysis researchers in academia and industry. The primary audience also comprises researchers and professionals in the fields of mathematics, AI, ML, deep learning, and computational modeling, including control theory, topology, nonlinear dynamical systems, computational statistics, and optimization
Table of contents
Table of contents
1. Mathematical Modeling and Optimization
2. Numerical Analysis and Scientific Computing
3. Machine Learning and Artificial Intelligence
4. Big Data Analytics and Visualization
5. Statistical Methods and Applications
6. Econometric Methods and Applications
7. Data-Driven Decision Making
8. Interdisciplinary Applications
9. Computational and Mathematical Neuroscience & Medicine
10. Emerging Trends in Mathematics and Data Science
11. Data Science in Healthcare and Medicine
12. Data Science in Finance and Marketing
13. Data Science in Engineering
14. Data Science in Energy Management
15. Data Science in Education and Natural Sciences
2. Numerical Analysis and Scientific Computing
3. Machine Learning and Artificial Intelligence
4. Big Data Analytics and Visualization
5. Statistical Methods and Applications
6. Econometric Methods and Applications
7. Data-Driven Decision Making
8. Interdisciplinary Applications
9. Computational and Mathematical Neuroscience & Medicine
10. Emerging Trends in Mathematics and Data Science
11. Data Science in Healthcare and Medicine
12. Data Science in Finance and Marketing
13. Data Science in Engineering
14. Data Science in Energy Management
15. Data Science in Education and Natural Sciences
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 1, 2027
- Language: English
About the editors
About the editors
MY
Mehmet Yavuz
Dr. Mehmet Yavuz received his Ph.D. degree in Applied Mathematics from Balikesir University, Turkey. He conducted postdoctoral research in the Department of Mathematics at the University of Exeter, U.K., in 2019-2020. He is currently an Associate Professor in the Mathematics and Computer Sciences Department at Necmettin Erbakan University, Turkey. He has published more than 70 research papers in reputed journals and 60 conference papers as well as 6 book chapters in international books. He is the Editor of Fractional Calculus: New Applications in Understanding Nonlinear Phenomena, Bentham Books. His research interests include fractional calculus and its applications to the different fields of science, mathematical biology, nonlinear dynamics, and optimal control. Apart from being an Associate Editor of several esteemed journals, he is the Editor-in-Chief of Mathematical Modelling and Numerical Simulation with Applications.
Affiliations and expertise
Associate Professor, Department of Mathematics and Computer Sciences, Faculty of Science, Necmettin Erbakan University, Konya, TurkeyFU
Fuat Usta
Dr. Fuat Usta received his BSc (Mathematical Engineering) degree from Istanbul Technical University, Türkiye in 2009 and MSc (Mathematical Finance) from University of Birmingham, UK in 2011 and PhD (Applied Mathematics) from University of Leicester, UK in 2015. At present, he is working as a Professor in the Department of Mathematics at Düzce University (Türkiye). He is interested in Approximation Theory, Multivariate approximation using Quasi Interpolation, Radial Basis Functions and Hierarchical/Wavelet Bases, High-Dimensional Approximation using Sparse Grids. Financial Mathematics, Integral Equations, Fractional Calculus, Partial Differential Equations.
Affiliations and expertise
Department of Mathematics, Düzce University, Düzce, TurkiyeCY
Ceylan Yozgatlıgil
Dr. Ceylan Yozgatlıgil is a Full Professor in the Department of Statistics at Middle East Technical University (METU), Ankara. She earned her Ph.D. in Statistics from Temple University, USA, where she specialized in temporal aggregation and multivariate time series analysis. Her research spans time series modeling, forecasting with machine and deep learning, temporal aggregation, anomaly detection, and applied data science.
With extensive academic and administrative experience, Dr. Yozgatlıgil has served as Vice-chair of the METU Statistics Department and Associate Director of the Institute of Applied Mathematics. She has authored numerous journal articles and conference papers on statistical modeling, climate and environmental data analysis, and computational methods.
Her recent work integrates artificial intelligence into forecasting and anomaly detection, with applications in climatology, energy systems, and industrial efficiency. Dr. Yozgatlıgil is actively involved in national and international projects, including TÜBİTAK-funded research and consultancy for organizations such as UNICEF and GIZ. Beyond her academic roles, she contributes to professional associations and promotes the advancement of data-driven research in Turkey and beyond
Affiliations and expertise
Department of Statistics, METU, Ankara, Türkiye