Data Science Theory And Applications

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Data Science: Theory and Applications

Author :
Publisher : North Holland
ISBN 13 : 0323852009
Total Pages : 348 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Data Science: Theory and Applications by :

Download or read book Data Science: Theory and Applications written by and published by North Holland. This book was released on 2021-03-03 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science: Theory and Applications, Volume 44 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of interesting topics, including Modeling extreme climatic events using the generalized extreme value distribution, Bayesian Methods in Data Science, Mathematical Modeling in Health Economic Evaluations, Data Science in Cancer Genomics, Blockchain Technology: Theory and Practice, Statistical outline of animal home ranges, an application of set estimation, Application of Data Handling Techniques to Predict Pavement Performance, Analysis of individual treatment effects for enhanced inferences in medicine, and more. Additional sections cover Nonparametric Data Science: Testing Hypotheses in Large Complex Data, From Urban Mobility Problems to Data Science Solutions, and Data Structures and Artificial Intelligence Methods. 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 Data Science: Theory and Applications

Data Science

Author : Qurban A Memon
Publisher : CRC Press
ISBN 13 : 0429554354
Total Pages : 345 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Data Science by : Qurban A Memon

Download or read book Data Science written by Qurban A Memon and published by CRC Press. This book was released on 2019-09-26 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: • Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. • Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. • Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science.

Data Science

Author : Gyanendra K. Verma
Publisher : Springer Nature
ISBN 13 : 9811616817
Total Pages : 444 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Data Science by : Gyanendra K. Verma

Download or read book Data Science written by Gyanendra K. Verma and published by Springer Nature. This book was released on 2021-08-19 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.

Trends of Data Science and Applications

Author : Siddharth Swarup Rautaray
Publisher : Springer Nature
ISBN 13 : 9813368152
Total Pages : 341 pages
Book Rating : 4.8/5 (133 download)

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Book Synopsis Trends of Data Science and Applications by : Siddharth Swarup Rautaray

Download or read book Trends of Data Science and Applications written by Siddharth Swarup Rautaray and published by Springer Nature. This book was released on 2021-03-21 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional.

Data Science in Theory and Practice

Author : Maria Cristina Mariani
Publisher : John Wiley & Sons
ISBN 13 : 1119674689
Total Pages : 404 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Data Science in Theory and Practice by : Maria Cristina Mariani

Download or read book Data Science in Theory and Practice written by Maria Cristina Mariani and published by John Wiley & Sons. This book was released on 2021-10-12 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

Machine Learning and Data Science

Author : Prateek Agrawal
Publisher : John Wiley & Sons
ISBN 13 : 1119776473
Total Pages : 276 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Machine Learning and Data Science by : Prateek Agrawal

Download or read book Machine Learning and Data Science written by Prateek Agrawal and published by John Wiley & Sons. This book was released on 2022-07-25 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: MACHINE LEARNING AND DATA SCIENCE Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia. Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms. These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.

Financial Data Analytics

Author : Sinem Derindere Köseoğlu
Publisher : Springer Nature
ISBN 13 : 3030837998
Total Pages : 393 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Financial Data Analytics by : Sinem Derindere Köseoğlu

Download or read book Financial Data Analytics written by Sinem Derindere Köseoğlu and published by Springer Nature. This book was released on 2022-04-25 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics.

Network Models for Data Science

Author : Alan Julian Izenman
Publisher : Cambridge University Press
ISBN 13 : 1108889034
Total Pages : 502 pages
Book Rating : 4.1/5 (88 download)

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Book Synopsis Network Models for Data Science by : Alan Julian Izenman

Download or read book Network Models for Data Science written by Alan Julian Izenman and published by Cambridge University Press. This book was released on 2023-01-05 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component.

Big Data Analytics in Supply Chain Management

Author : Iman Rahimi
Publisher : CRC Press
ISBN 13 : 1000326918
Total Pages : 211 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Big Data Analytics in Supply Chain Management by : Iman Rahimi

Download or read book Big Data Analytics in Supply Chain Management written by Iman Rahimi and published by CRC Press. This book was released on 2020-12-20 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems.

Compositional Data Analysis

Author : Vera Pawlowsky-Glahn
Publisher : John Wiley & Sons
ISBN 13 : 0470711353
Total Pages : 401 pages
Book Rating : 4.4/5 (77 download)

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Book Synopsis Compositional Data Analysis by : Vera Pawlowsky-Glahn

Download or read book Compositional Data Analysis written by Vera Pawlowsky-Glahn and published by John Wiley & Sons. This book was released on 2011-09-19 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more difficult to realize that so many statisticians and users of statistics are unaware of the particular problems affecting compositional data, as well as their solutions. The issue of ``spurious correlation'', as the situation was phrased by Karl Pearson back in 1897, affects all data that measures parts of some whole, such as percentages, proportions, ppm and ppb. Such measurements are present in all fields of science, ranging from geology, biology, environmental sciences, forensic sciences, medicine and hydrology. This book presents the history and development of compositional data analysis along with Aitchison's log-ratio approach. Compositional Data Analysis describes the state of the art both in theoretical fields as well as applications in the different fields of science. Key Features: Reflects the state-of-the-art in compositional data analysis. Gives an overview of the historical development of compositional data analysis, as well as basic concepts and procedures. Looks at advances in algebra and calculus on the simplex. Presents applications in different fields of science, including, genomics, ecology, biology, geochemistry, planetology, chemistry and economics. Explores connections to correspondence analysis and the Dirichlet distribution. Presents a summary of three available software packages for compositional data analysis. Supported by an accompanying website featuring R code. Applied scientists working on compositional data analysis in any field of science, both in academia and professionals will benefit from this book, along with graduate students in any field of science working with compositional data.

High-Dimensional Probability

Author : Roman Vershynin
Publisher : Cambridge University Press
ISBN 13 : 1108415199
Total Pages : 299 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis High-Dimensional Probability by : Roman Vershynin

Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

Data Science: New Issues, Challenges and Applications

Author : Gintautas Dzemyda
Publisher : Springer Nature
ISBN 13 : 3030392503
Total Pages : 325 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Data Science: New Issues, Challenges and Applications by : Gintautas Dzemyda

Download or read book Data Science: New Issues, Challenges and Applications written by Gintautas Dzemyda and published by Springer Nature. This book was released on 2020-02-13 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains 16 chapters by researchers working in various fields of data science. They focus on theory and applications in language technologies, optimization, computational thinking, intelligent decision support systems, decomposition of signals, model-driven development methodologies, interoperability of enterprise applications, anomaly detection in financial markets, 3D virtual reality, monitoring of environmental data, convolutional neural networks, knowledge storage, data stream classification, and security in social networking. The respective papers highlight a wealth of issues in, and applications of, data science. Modern technologies allow us to store and transfer large amounts of data quickly. They can be very diverse - images, numbers, streaming, related to human behavior and physiological parameters, etc. Whether the data is just raw numbers, crude images, or will help solve current problems and predict future developments, depends on whether we can effectively process and analyze it. Data science is evolving rapidly. However, it is still a very young field. In particular, data science is concerned with visualizations, statistics, pattern recognition, neurocomputing, image analysis, machine learning, artificial intelligence, databases and data processing, data mining, big data analytics, and knowledge discovery in databases. It also has many interfaces with optimization, block chaining, cyber-social and cyber-physical systems, Internet of Things (IoT), social computing, high-performance computing, in-memory key-value stores, cloud computing, social computing, data feeds, overlay networks, cognitive computing, crowdsource analysis, log analysis, container-based virtualization, and lifetime value modeling. Again, all of these areas are highly interrelated. In addition, data science is now expanding to new fields of application: chemical engineering, biotechnology, building energy management, materials microscopy, geographic research, learning analytics, radiology, metal design, ecosystem homeostasis investigation, and many others.

Data Science: Theory and Applications

Author :
Publisher : Elsevier
ISBN 13 : 0323852017
Total Pages : 350 pages
Book Rating : 4.3/5 (238 download)

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Book Synopsis Data Science: Theory and Applications by :

Download or read book Data Science: Theory and Applications written by and published by Elsevier. This book was released on 2021-02-12 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science: Theory and Applications, Volume 44 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on a variety of interesting topics, including Modeling extreme climatic events using the generalized extreme value distribution, Bayesian Methods in Data Science, Mathematical Modeling in Health Economic Evaluations, Data Science in Cancer Genomics, Blockchain Technology: Theory and Practice, Statistical outline of animal home ranges, an application of set estimation, Application of Data Handling Techniques to Predict Pavement Performance, Analysis of individual treatment effects for enhanced inferences in medicine, and more. Additional sections cover Nonparametric Data Science: Testing Hypotheses in Large Complex Data, From Urban Mobility Problems to Data Science Solutions, and Data Structures and Artificial Intelligence Methods. 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 Data Science: Theory and Applications

Statistics for Beginners in Data Science

Author : Ai Publishing
Publisher :
ISBN 13 : 9781734790115
Total Pages : 188 pages
Book Rating : 4.7/5 (91 download)

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Book Synopsis Statistics for Beginners in Data Science by : Ai Publishing

Download or read book Statistics for Beginners in Data Science written by Ai Publishing and published by . This book was released on 2020-04-18 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics for Beginners in Data Science Statistical methods are an integral part of data science. Hence, a formal training in statistics is indispensable for data scientists. If you are keen on getting your foot into the lucrative data science and analysis universe, you need to have a fundamental understanding of statistical analysis. Besides, Python is a versatile programming language you need to master to become a career data scientist. As a data scientist, you will identify, clean, explore, analyze, and interpret trends or possible patterns in complex data sets. The explosive growth of Big Data means you have to manage enormous amounts of data, clean it, manipulate it, and process it. Only then the most relevant data can be used. Python is a natural data science tool as it has an assortment of useful libraries, such as Pandas, NumPy, SciPy, Matplotlib, Seaborn, StatsModels, IPython, and several more. And Python's focus on simplicity makes it relatively easy for you to learn. Importantly, the ease of performing repetitive tasks saves you precious time. Long story short--Python is simply a high-priority data science tool. How Is This Book Different? The book focuses equally on the theoretical as well as practical aspects of data science. You will learn how to implement elementary data science tools and algorithms from scratch. The book contains an in-depth theoretical and analytical explanation of all data science concepts and also includes dozens of hands-on, real-life projects that will help you understand the concepts better. The ready-to-access Python codes at various places right through the book are aimed at shortening your learning curve. The main goal is to present you with the concepts, the insights, the inspiration, and the right tools needed to dive into coding and analyzing data in Python. The main benefit of purchasing this book is you get quick access to all the extra content provided with this book--Python codes, exercises, references, and PDFs--on the publisher's website, at no extra price. You get to experiment with the practical aspects of Data Science right from page 1. Beginners in Python and statistics will find this book extremely informative, practical, and helpful. Even if you aren't new to Python and data science, you'll find the hands-on projects in this book immensely helpful. The topics covered include: Introduction to Statistics Getting Familiar with Python Data Exploration and Data Analysis Pandas, Matplotlib, and Seaborn for Statistical Visualization Exploring Two or More Variables and Categorical Data Statistical Tests and ANOVA Confidence Interval Regression Analysis Classification Analysis Click the BUY button and download the book now to start learning and coding Python for Data Science.

Statistics for High-Dimensional Data

Author : Peter Bühlmann
Publisher : Springer Science & Business Media
ISBN 13 : 364220192X
Total Pages : 568 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Statistics for High-Dimensional Data by : Peter Bühlmann

Download or read book Statistics for High-Dimensional Data written by Peter Bühlmann and published by Springer Science & Business Media. This book was released on 2011-06-08 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.

Classification and Data Analysis

Author : Krzysztof Jajuga
Publisher : Springer Nature
ISBN 13 : 3030523489
Total Pages : 334 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Classification and Data Analysis by : Krzysztof Jajuga

Download or read book Classification and Data Analysis written by Krzysztof Jajuga and published by Springer Nature. This book was released on 2020-08-28 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume gathers peer-reviewed contributions on data analysis, classification and related areas presented at the 28th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2019, held in Szczecin, Poland, on September 18–20, 2019. Providing a balance between theoretical and methodological contributions and empirical papers, it covers a broad variety of topics, ranging from multivariate data analysis, classification and regression, symbolic (and other) data analysis, visualization, data mining, and computer methods to composite measures, and numerous applications of data analysis methods in economics, finance and other social sciences. The book is intended for a wide audience, including researchers at universities and research institutions, graduate and doctoral students, practitioners, data scientists and employees in public statistical institutions.

Foundations of Data Science

Author : Avrim Blum
Publisher : Cambridge University Press
ISBN 13 : 1108617360
Total Pages : 433 pages
Book Rating : 4.1/5 (86 download)

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Book Synopsis Foundations of Data Science by : Avrim Blum

Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.