Data Science in Theory and Practice

MARIANI TWENEBOAH BECCAR-VARELA 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, ...

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Author: Maria Cristina Mariani

Publisher: John Wiley & Sons

ISBN: 9781119674733

Category: Mathematics

Page: 404

View: 283

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.

Extending the Boundaries of Design Science Theory and Practice

In this paper we provide evidence of an approach organizations can take to deploy data science technologies ... 100 global advanced manufacturing company extended the boundary of DSR theory and practice through the development of a DS ...

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Author: Bengisu Tulu

Publisher: Springer

ISBN: 9783030195045

Category: Computers

Page: 317

View: 746

This book constitutes the thoroughly refereed proceedings of the 14th International Conference on Designing for a Digital and Globalized World, DESRIST 2019, held Worcester, MA, USA, June 2019. The 20 revised full papers included in the volume were carefully reviewed and selected from 54 submissions. They are organized in the following topical sections: Design Science Research Theory and Methodology; Design Science Research Applications in Healthcare; Design Science Research Applications in Data Science; and Design Science Research Applications in Emerging Topics.

Data Science Theory and Applications

2.3 Encryption methods The usual practice for a secure communication between entities/devices (of a system) is to encrypt messages. Being a decentralized network, blockchain system witnesses a lot of communication among its machines.

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Author:

Publisher: Elsevier

ISBN: 9780323852012

Category: Mathematics

Page: 348

View: 364

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

Cybersecurity Data Science

The data science body of theory breadth threatens pragmatic focus in CSDS and hence the ability to frame and adopt standard practices. The critique that the data science domain lacks rigor due to breadth can be addressed within CSDS by ...

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Author: Scott Mongeau

Publisher: Springer Nature

ISBN: 9783030748968

Category: Computers

Page: 388

View: 949

This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography.

Data Science in Theory and Practice

This book 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 ...

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Author: Maria C. Mariani

Publisher:

ISBN: 1119674751

Category: Mathematics

Page: 396

View: 853

"This textbook is dedicated to practitioners, graduate, and advanced undergraduate students who have interest in Data Science, Business analytics, and Statistical and Mathematical Modeling in different disciplines such as Finance, Geophysics, and Engineering. This book is designed to serve as a textbook for several courses in the aforementioned areas and a reference guide for practitioners in the industry. The book has a strong theoretical background and several applications to specific practical problems. It contains numerous techniques applicable to modern data science and other disciplines. In today's world, many fields are confronted with increasingly large amounts of complex data. Financial, healthcare, and geophysical data sampled with high frequency is no exception. These staggering amounts of data pose special challenges to the world of finance and other disciplines such as healthcare and geophysics, as traditional models and information technology tools can be poorly suited to grapple with their size and complexity. Probabilistic modeling, mathematical modeling, and statistical data analysis attempt to discover order from apparent disorder; this textbook may serve as a guide to various new systematic approaches on how to implement these quantitative activities with complex data sets."--

Roundtable on Data Science Postsecondary Education

60 ROUNDTABLE ON DATA SCIENCE POSTSECONDARY EDUCATION outlast changing technologies. As the gap between theory and practice begins to close, however, and undergraduate programs and opportunities change drastically, he wondered whether ...

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Author: National Academies of Sciences, Engineering, and Medicine

Publisher: National Academies Press

ISBN: 9780309677738

Category: Education

Page: 223

View: 120

Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.

Principles and Methods for Data Science

... hybrid models, Markov Chain Monte Carlo methods: theory and practice, and more. Principles and Methods for Data Science has been developed with brilliantly written chapters by authors from various aspects of data science.

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Author:

Publisher: Elsevier

ISBN: 9780444642127

Category: Mathematics

Page: 496

View: 213

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

Social Media In Sport Theory And Practice

SOCIAL MEDIA DATA ANALYTICS PRACTICES The analysis of social media content has become a core activity owing to the availability of enormous amounts of data through web-based application programming interfaces. Analytics refers to ...

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Author: Gashaw Abeza

Publisher: World Scientific

ISBN: 9789811237676

Category: Business & Economics

Page: 504

View: 326

This book enables students to grasp the holistic enterprise of social media as it pertains to social, legal, marketing, and management issues. The book also helps students better understand the research process in social media scholarship and make connections with academic research and applied practice in sport studies.

Human Centered Data Science

“ Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool . " In Genetic Programming Theory and Practice XIV , edited by Rick Riolo , Bill Worzel , Brian ...

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Author: Cecilia Aragon

Publisher: MIT Press

ISBN: 9780262367592

Category: Computers

Page: 200

View: 644

Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.

Data Science for Business

As we have illustrated throughout the book, these principles not only allow us to understand the theory and practice of data science much more deeply, they also allow us to understand the methods and techniques of data science very ...

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Author: Foster Provost

Publisher: "O'Reilly Media, Inc."

ISBN: 9781449374297

Category: Business & Economics

Page: 414

View: 680

Annotation This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques.