Advanced Methods in Mathematics and Data Science

Concepts and Applications

Häftad, Engelska, 2027

1 963 kr

Kommande

Beskrivning

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.

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

Produktinformation

Utgivningsdatum:2027-02-01
Mått:191 x 235 x undefined mm
Format:Häftad
Språk:Engelska
Antal sidor:250
Förlag:Elsevier Science
ISBN:9780443511219

Utforska kategorier

Referensverk och tvärvetenskap inom Samhälle och politik
Databaser inom Data och IT
Artificiell intelligens inom Data och IT

Mer om författaren

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. 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. 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

Innehållsförteckning

1. Mathematical Modeling and Optimization2. Numerical Analysis and Scientific Computing3. Machine Learning and Artificial Intelligence4. Big Data Analytics and Visualization5. Statistical Methods and Applications6. Econometric Methods and Applications7. Data-Driven Decision Making8. Interdisciplinary Applications9. Computational and Mathematical Neuroscience & Medicine10. Emerging Trends in Mathematics and Data Science11. Data Science in Healthcare and Medicine12. Data Science in Finance and Marketing13. Data Science in Engineering14. Data Science in Energy Management15. Data Science in Education and Natural Sciences