- Recording, musical
Authors: Dan Coates (1999) - Responding to popular demand, Dan Coates has compiled a complete collection of 77 great songs for all occasions, to be played by the advanced piano player. Titles include love and wedding favorites, Broadway standards, pop and country ballads, movie and TV hits, and many more. Two selections Colors of the Wind and Star Wars are included on the Royal Conservatory of Music Popular Selection List (2009 Ed.) (These arrangements were originally published in item #AF9842.)
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Authors: - (-) - This programme includes a summary of that process and explores specific areas of data analysis that might be applicable to learners studying at undergraduate and post graduate levels. It aims to provide a definition of qualitative and quantitative data analysis and opportunities to explore photographic, video, textual and numerical data analysis through providing worked examples and further opportunities for learners to develop knowledge and skills in data analysis. The programme also aims to support the development of critical appraisal skills, thorough considering the critical review of research papers. The aims are achieved through captialising on the interactive opportunities of o...
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Authors: Gábor Békés (2023) - This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy.
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Authors: - (-) - Data Analysis, Statistics, and Probability introduces statistics as a problem-solving process. In this course, you will build your skills through investigations of different ways to collect and represent data and to analyze and interpret variation in data. The course consists of 10 sessions, each with a half hour of video programming, problem-solving activities provided online and in a print guide, and interactive activities and demonstrations on the Web.
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- Book
Authors: William Stallings (2007) - This timely revision of an all-time best-seller in the field features the clarity and scope of a Stallings classic. This comprehensive volume provides the most up-to-date coverage of the essential topics in data communications, networking, Internet technology and protocols, and standards – all in a convenient modular format. Features updated coverage of multimedia, Gigabit and 10 Gbps Ethernet, WiFi/IEEE 802.11 wireless LANs, security, and much more. Ideal for professional reference or self-study. For Product Development personnel, Programmers, Systems Engineers, Network Designers and others involved in the design of data communications and networking products.
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- Book
Authors: Michael Duck (2003) - A broad introductory text written for computer scientists and engineers, focusing on the fundamental aspects of data communications and computer networks. This second edition has been thoroughly revised to cover current networking issues and technologies. Specific updates include those on networks and their management, transmission technologies, security and encryption. This text is appropriate for modular courses in data communications as well as computer science. It is also a useful source for professionals in related disciplines.
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Authors: William W. Cooper (2000) - Each chapter accompanies its developments with simple numerical examples and discussions of actual applications. Emphasis is placed on the use as well as an understanding of DEA and the topics in this book have been selected and treated accordingly. The objective is to introduce students, researchers, and practitioners in business, economics, engineering, and the sciences to Data Envelopment Analysis.
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Authors: Abdelghny Orogat (-) - Data Lake, as defined in the IBM Redbook, is a set of centralized repositories containing vast amounts of raw data (either structured or unstructured), described by metadata, organized into identifi- able datasets, and available on demand.
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Authors: Ian H. Witten (2005) - -
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Authors: Ian H. Witten (2017) - Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations.
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Authors: Ian H. Witten (2011) - Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.
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Authors: Galit Shmueli (2020) - This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis.
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Authors: Daniel T. Larose (2006) - Data Mining Methods and Models continues the thrust of Discovering Knowledge in Data, providing the reader with: - Models and techniques to uncover hidden nuggets of information - Insight into how the data mining algorithms really work - Experience of actually performing data mining on large data sets
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- Book
Authors: Jiawei Han (2012) - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
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- Book
Authors: Jiawei Han (2022) - Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD.
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- Book
Authors: Dimitri Bertsekas; Robert Gallager (1987) - -
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- Book
Authors: Dimitri Bertsekas (1992) - This volume is designed to help professionals develop a deeper understanding of data networks and evolving integrated networks, and to explore today's various analysis and design tools.
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Authors: Tobias Naef (2023) - This open access book offers a new account on the legal conflict between privacy and trade in the digital sphere. It develops a fundamental rights theory with a new right to continuous protection of personal data and explores the room for the application of this new right in trade law. Replicable legal analysis and practical solutions show the way to deal with cross-border data flows without violating fundamental rights and trade law principles.
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Authors: Prashanth H. Southekal (2023) - Quality data is the key for business enterprises to offer improved performance in operations, compliance, and decision making. According to McKinsey, data driven organizations provide EBITDA increases between 15 to 25% than peers. However, to be a data driven organization, data quality is very important. But most companies are plagued with poor data quality. A HBR study found that just 3% of the data in a business enterprise meets quality standards. According to Gartner, 27% of data in the world's top companies is flawed--so companies are looking for practical guidance to improve data quality. This book examines the four-phase DARS approach (Define-Assess-Realize-Sustain) for companie...
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Authors: Barr Moses (2022) - "Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you. Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used...
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