# Mathematical Methods In Data Science

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### Mathematical Methods in Data Science

Author | : Jingli Ren,Haiyan Wang |

Publsiher | : Elsevier |

Total Pages | : 260 |

Release | : 2023-01-06 |

Genre | : Computers |

ISBN | : 9780443186806 |

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Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authorsâ€™ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science. Combines a broad spectrum of mathematics, including linear algebra, optimization, network analysis and ordinary and partial differential equations for data science Written by two researchers who are actively applying mathematical and statistical methods as well as ODE and PDE for data analysis and prediction Highly interdisciplinary, with content spanning mathematics, data science, social media analysis, network science, financial markets, and more Presents a wide spectrum of topics in a logical order, including probability, linear algebra, calculus and optimization, networks, ordinary differential and partial differential equations

### Data Science and Machine Learning

Author | : Dirk P. Kroese,Zdravko Botev,Thomas Taimre,Radislav Vaisman |

Publsiher | : CRC Press |

Total Pages | : 538 |

Release | : 2019-11-20 |

Genre | : Business & Economics |

ISBN | : 9781000730777 |

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Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

### Mathematical Methods for Knowledge Discovery and Data Mining

Author | : Felici, Giovanni,Vercellis, Carlo |

Publsiher | : IGI Global |

Total Pages | : 394 |

Release | : 2007-10-31 |

Genre | : Computers |

ISBN | : 9781599045306 |

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"This book focuses on the mathematical models and methods that support most data mining applications and solution techniques, covering such topics as association rules; Bayesian methods; data visualization; kernel methods; neural networks; text, speech, and image recognition; an invaluable resource for scholars and practitioners in the fields of biomedicine, engineering, finance, manufacturing, marketing, performance measurement, and telecommunications"--Provided by publisher.

### Mathematical Problems in Data Science

Author | : Li M. Chen,Zhixun Su,Bo Jiang |

Publsiher | : Springer |

Total Pages | : 219 |

Release | : 2015-12-15 |

Genre | : Computers |

ISBN | : 9783319251271 |

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This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.

### The Mathematics of Data

Author | : Michael W. Mahoney,John C. Duchi,Anna C. Gilbert |

Publsiher | : American Mathematical Soc. |

Total Pages | : 325 |

Release | : 2018-11-15 |

Genre | : Big data |

ISBN | : 9781470435752 |

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### Principles and Methods for Data Science

Author | : Anonim |

Publsiher | : Elsevier |

Total Pages | : 498 |

Release | : 2020-05-28 |

Genre | : Mathematics |

ISBN | : 9780444642127 |

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Principles and Methods for Data Science, Volume 43 in the Handbook of Statistics series, highlights new advances in the field, with this updated volume presenting interesting and timely topics, including Competing risks, aims and methods, Data analysis and mining of microbial community dynamics, Support Vector Machines, a robust prediction method with applications in bioinformatics, Bayesian Model Selection for Data with High Dimension, High dimensional statistical inference: theoretical development to data analytics, Big data challenges in genomics, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods: Theory and Practice, and more. Provides the authority and expertise of leading contributors from an international board of authors Presents the latest release in the Handbook of Statistics series Updated release includes the latest information on Principles and Methods for Data Science

### Simulation and Analysis of Mathematical Methods in Real Time Engineering Applications

Author | : T. Ananth Kumar,E. Golden Julie,Y. Harold Robinson,S. M. Jaisakthi |

Publsiher | : John Wiley & Sons |

Total Pages | : 370 |

Release | : 2021-08-16 |

Genre | : Mathematics |

ISBN | : 9781119785507 |

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SIMULATIONS AND ANALYSIS of Mathematical Methods Written and edited by a group of international experts in the field, this exciting new volume covers the state of the art of real-time applications of computer science using mathematics. This breakthrough edited volume highlights the security, privacy, artificial intelligence, and practical approaches needed by engineers and scientists in all fields of science and technology. It highlights the current research, which is intended to advance not only mathematics but all areas of science, research, and development, and where these disciplines intersect. As the book is focused on emerging concepts in machine learning and artificial intelligence algorithmic approaches and soft computing techniques, it is an invaluable tool for researchers, academicians, data scientists, and technology developers. The newest and most comprehensive volume in the area of mathematical methods for use in real-time engineering, this groundbreaking new work is a must-have for any engineer or scientistâ€™s library. Also useful as a textbook for the student, it is a valuable contribution to the advancement of the science, both a working handbook for the new hire or student, and a reference for the veteran engineer.

### Mathematical Foundations of Data Science Using R

Author | : Frank Emmert-Streib,Salissou Moutari,Matthias Dehmer |

Publsiher | : Walter de Gruyter GmbH & Co KG |

Total Pages | : 444 |

Release | : 2022-10-24 |

Genre | : Computers |

ISBN | : 9783110796179 |

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The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.