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Author: Paul Murrell
Publisher: CRC Press
ISBN: 1420065181
Category : Mathematics
Languages : en
Pages : 445
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Book Description
Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on computational tools, the book shows readers how to improve thei
Author: Paul Murrell
Publisher: CRC Press
ISBN: 1420065181
Category : Mathematics
Languages : en
Pages : 445
Get Book Here
Book Description
Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on computational tools, the book shows readers how to improve thei
Author: Francesco Corea
Publisher: Springer
ISBN: 3030044688
Category : Technology & Engineering
Languages : en
Pages : 131
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Book Description
This book reflects the author’s years of hands-on experience as an academic and practitioner. It is primarily intended for executives, managers and practitioners who want to redefine the way they think about artificial intelligence (AI) and other exponential technologies. Accordingly the book, which is structured as a collection of largely self-contained articles, includes both general strategic reflections and detailed sector-specific information. More concretely, it shares insights into what it means to work with AI and how to do it more efficiently; what it means to hire a data scientist and what new roles there are in the field; how to use AI in specific industries such as finance or insurance; how AI interacts with other technologies such as blockchain; and, in closing, a review of the use of AI in venture capital, as well as a snapshot of acceleration programs for AI companies.
Author: Deborah Nolan
Publisher: Springer Science & Business Media
ISBN: 1461479002
Category : Computers
Languages : en
Pages : 677
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Book Description
Web technologies are increasingly relevant to scientists working with data, for both accessing data and creating rich dynamic and interactive displays. The XML and JSON data formats are widely used in Web services, regular Web pages and JavaScript code, and visualization formats such as SVG and KML for Google Earth and Google Maps. In addition, scientists use HTTP and other network protocols to scrape data from Web pages, access REST and SOAP Web Services, and interact with NoSQL databases and text search applications. This book provides a practical hands-on introduction to these technologies, including high-level functions the authors have developed for data scientists. It describes strategies and approaches for extracting data from HTML, XML, and JSON formats and how to programmatically access data from the Web. Along with these general skills, the authors illustrate several applications that are relevant to data scientists, such as reading and writing spreadsheet documents both locally and via Google Docs, creating interactive and dynamic visualizations, displaying spatial-temporal displays with Google Earth, and generating code from descriptions of data structures to read and write data. These topics demonstrate the rich possibilities and opportunities to do new things with these modern technologies. The book contains many examples and case-studies that readers can use directly and adapt to their own work. The authors have focused on the integration of these technologies with the R statistical computing environment. However, the ideas and skills presented here are more general, and statisticians who use other computing environments will also find them relevant to their work. Deborah Nolan is Professor of Statistics at University of California, Berkeley. Duncan Temple Lang is Associate Professor of Statistics at University of California, Davis and has been a member of both the S and R development teams.
Author: Chirag Shah
Publisher: Cambridge University Press
ISBN: 1108472443
Category : Business & Economics
Languages : en
Pages : 459
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Book Description
An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.
Author: Anthony David Giordano
Publisher: Fulton Books, Inc.
ISBN:
Category : Computers
Languages : en
Pages : 200
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Book Description
Digital, cloud, and artificial intelligence (AI) have disrupted how we use data. This disruption has changed the way we need to provision, curate, and publish data for the multiple use cases in today's technology-driven environment. This text will cover how to design, develop, and evolve a data platform for all the uses of enterprise data needed in today's digital organization. This book focuses on explaining what a data platform is, what value it provides, how is it engineered, and how to deploy a data platform and support organization. In this context, Introduction to Data Platforms reviews the current requirements for data in the digital age and quantifies the use cases; discusses the evolution of data over the past twenty years, which is a core driver of the modern data platform; defines what a data platform is and defines the architectural components and layers of a data platform; provides the architectural layers or capabilities of a data platform; reviews cloud- and commercial-software vendors that populate the data-platform space; provides a step-by-step approach to engineering, deploying, supporting, and evolving a data-platform environment; provides a step-by-step approach to migrating legacy data warehouses, data marts, and data lakes/sandboxes to a data platform; and reviews organizational structures for managing data platform environments.
Author: João Moreira
Publisher: John Wiley & Sons
ISBN: 1119296242
Category : Mathematics
Languages : en
Pages : 352
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Book Description
A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-to-understand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples. Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer: A guide to the reasoning behind data mining techniques A unique illustrative example that extends throughout all the chapters Exercises at the end of each chapter and larger projects at the end of each of the text’s two main parts Together with these learning resources, the book can be used in a 13-week course guide, one chapter per course topic. The book was written in a format that allows the understanding of the main data analytics concepts by non-mathematicians, non-statisticians and non-computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.
Author: Foster Provost
Publisher: "O'Reilly Media, Inc."
ISBN: 144937428X
Category : Computers
Languages : en
Pages : 506
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Book Description
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces 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. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates
Author: Borko Furht
Publisher: Springer
ISBN: 3319445502
Category : Computers
Languages : en
Pages : 405
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Book Description
The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems®) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.
Author: Carlisle Adams
Publisher: Springer Nature
ISBN: 3030810437
Category : Computers
Languages : en
Pages : 328
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Book Description
This textbook provides a unique lens through which the myriad of existing Privacy Enhancing Technologies (PETs) can be easily comprehended and appreciated. It answers key privacy-centered questions with clear and detailed explanations. Why is privacy important? How and why is your privacy being eroded and what risks can this pose for you? What are some tools for protecting your privacy in online environments? How can these tools be understood, compared, and evaluated? What steps can you take to gain more control over your personal data? This book addresses the above questions by focusing on three fundamental elements: It introduces a simple classification of PETs that allows their similarities and differences to be highlighted and analyzed; It describes several specific PETs in each class, including both foundational technologies and important recent additions to the field; It explains how to use this classification to determine which privacy goals are actually achievable in a given real-world environment. Once the goals are known, this allows the most appropriate PETs to be selected in order to add the desired privacy protection to the target environment. To illustrate, the book examines the use of PETs in conjunction with various security technologies, with the legal infrastructure, and with communication and computing technologies such as Software Defined Networking (SDN) and Machine Learning (ML). Designed as an introductory textbook on PETs, this book is essential reading for graduate-level students in computer science and related fields, prospective PETs researchers, privacy advocates, and anyone interested in technologies to protect privacy in online environments.
Author: Alex Gorelik
Publisher: "O'Reilly Media, Inc."
ISBN: 1491931507
Category : Computers
Languages : en
Pages : 232
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Book Description
The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries
