Multidimensional Data Visualization

Data, Matter, Design presents a comprehensive overview of current design processes that rely on the input of data and use of computational design strategies, and their relationship to an array of

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.

Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.

Learn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine le

Data has become a social and political issue because of its capacity to reconfigure relationships between states, subjects, and citizens. This book explores how data has acquired such an important cap

Since the early 2000s, digital data has transformed the way we live and work. This timely book looks to big data analytics to understand this revolutionary change, unpacking the impact of big data ana

How to create an Internet of Trusted Data in which insights from data can be extracted without collecting, holding, or revealing the underlying data.Trusted Data describes a data architecture that pla

Avoid data blunders and create truly useful visualizations Avoiding Data Pitfalls is a reputation-saving handbook for those who work with data, designed to help you avoid the all-too-common blunders t

Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one

What if you sliced data and found a huge problem or opportunity? Data did its job, but now it needs a storyteller. The way insights are communicated could reverse or increase the trajectory of data. T

The Handbook of Green Economics reveals the breadth and depth of advanced research on sustainability and growth while identifying opportunities for future developments. Through its multidimensional ex

Teachers handle student data every day, but too often they are not provided with the guidance they need to protect student data privacy.

Teachers handle student data every day, but too often they are not provided with the guidance they need to protect student data privacy.

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. Th

This volume will assist readers in fitting big data analysis into their service-based organizations.Volume I of this two-volume series focuses on the role of big data in service delivery systems. It d

This text closely examines ideas, analysis, and implementation details of data structures as a specialised topic in applied algorithms. It looks at efficient ways to realise query and update operations on sets of numbers, intervals, or strings by various data structures, including: search trees; structures for sets of intervals or piece-wise constant functions; orthogonal range search structures; heaps; union-find structures; dynamization and persistence of structures; structures for strings; and hash tables. Instead of relegating data structures to trivial material used to illustrate object-oriented programming methodology, this is the first volume to show data structures as a crucial algorithmic topic. Numerous code examples in C and more than 500 references make Advanced Data Structures an indispensable text.

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. W

How to analyze data settings rather than data sets, acknowledging the meaning-making power of the local.In our data-driven society, it is too easy to assume the transparency of data. Instead, Yanni Lo

A groundbreaking, flexible approach to computer science and data scienceThe Deitels’ Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud o

Master data analysis, modeling and the effective use of spreadsheets with the popular BUSINESS ANALYTICS: DATA ANALYSIS AND DECISION MAKING, 7E. The quantitative methods approach in this edition helps

Data has become a social and political issue because of its capacity to reconfigure relationships between states, subjects, and citizens. This book explores how data has acquired such an important cap

The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational

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 scien

Creating Inclusive Schools aims to simplify the dynamic and multidimensional discipline of Inclusive education for the students of education, and trainee and practising teachers. This book focuses on

This book is about constructing models from experimental data. It covers a range of topics, from statistical data prediction to Kalman filtering, from black-box model identification to parameter estim

Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapt

Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are

Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scala

Big Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Proce

Reveals the occult wisdom and multidimensional layers of meaning hidden in the Nordic Rune stones • Explores the practice of the Uthark divination system encoded within the traditional exoteric Futhar

In the context of the Sustainable Development Goals (SDGs), the Livingstone declaration, and the UN Social Protection Floor, this book deals jointly with multidimensional child poverty and social prot

Firms are collecting and analyzing customer data at an ever increasing rate in response to evidence that data analytics (precision targeting, improved selling) generates a positive return. Yet e

This book looks at various application and data demand drivers, along with data infrastructure options from legacy on premise, public cloud, hybrid, software-defined data center (SDDC), software data

Haunted Data explores the concepts that are at work in our complex relationships with data. Our engagement with data – big or small – is never as simplistic or straightforward as might first appear. I

This book examines the domain of human agency-environment interaction from a multidimensional point of view. It explores the human-environment interface by analysing its ethical, political and epistem

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax ba

Human resources is rapidly evolving into a data-rich field but with big data comes big decisions. The best companies understand how to use data to make strategic workforce decisions and gain significa

Human resources is rapidly evolving into a data-rich field but with big data comes big decisions. The best companies understand how to use data to make strategic workforce decisions and gain significa

While traditional databases excel at complex queries over historical data, they are inherently pull-based and therefore ill-equipped to push new information to clients. Systems for data stream managem

"This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science