Web And Network Data Science

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Web And Network Data Science

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Author : Thomas W. Miller
language : en
Publisher: FT Press
Release Date : 2014-12-19

Web And Network Data Science written by Thomas W. Miller and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-19 with Business & Economics categories.

Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

Data Science And Complex Networks

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Author : Guido Caldarelli
language : en
Publisher: Oxford University Press
Release Date : 2016-11-10

Data Science And Complex Networks written by Guido Caldarelli and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Science categories.

This book provides a comprehensive yet short description of the basic concepts of Complex Network theory. In contrast to other books the authors present these concepts through real case studies. The application topics span from Foodwebs, to the Internet, the World Wide Web and the Social Networks, passing through the International Trade Web and Financial time series. The final part is devoted to definition and implementation of the most important network models. The text provides information on the structure of the data and on the quality of available datasets. Furthermore it provides a series of codes to allow immediate implementation of what is theoretically described in the book. Readers already used to the concepts introduced in this book can learn the art of coding in Python by using the online material. To this purpose the authors have set up a dedicated web site where readers can download and test the codes. The whole project is aimed as a learning tool for scientists and practitioners, enabling them to begin working instantly in the field of Complex Networks.

Foundations Of Data Science

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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23

Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.

Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.

Network Science

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Author : Albert-László Barabási
language : en
Publisher: Cambridge University Press
Release Date : 2016-07-21

Network Science written by Albert-László Barabási and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-21 with Computers categories.

Illustrated throughout in full colour, this pioneering text is the only book you need for an introduction to network science.

Doing Data Science

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Author : Cathy O'Neil
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2013-10-09

Doing Data Science written by Cathy O'Neil and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-09 with Computers categories.

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

Data Science And Data Analytics

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Author : Amit Kumar Tyagi
language : en
Publisher: CRC Press
Release Date : 2021-09-22

Data Science And Data Analytics written by Amit Kumar Tyagi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-22 with Computers categories.

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues. Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc. Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity.

Data Science And Big Data Analytics In Smart Environments

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Author : Marta Chinnici
language : en
Publisher: CRC Press
Release Date : 2021-07-28

Data Science And Big Data Analytics In Smart Environments written by Marta Chinnici and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-28 with Computers categories.

Most applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment. Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.

Data Science Using Python And R

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Author : Chantal D. Larose
language : en
Publisher: John Wiley & Sons
Release Date : 2019-04-09

Data Science Using Python And R written by Chantal D. Larose and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-09 with Computers categories.

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

Social Network Data Analytics

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Author : Charu C. Aggarwal
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-18

Social Network Data Analytics written by Charu C. Aggarwal and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-18 with Computers categories.

Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

Modeling Techniques In Predictive Analytics

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Author : Thomas W. Miller
language : en
Publisher: FT Press
Release Date : 2014-09-29

Modeling Techniques In Predictive Analytics written by Thomas W. Miller and has been published by FT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-29 with Business & Economics categories.

To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more