This textbook presents the main principles of visual analytics and describes techniques and approaches that have proven their utility and can be readily reproduced. Special emphasis is placed on various instructive examples of analyses, in which the need for and the use of visualisations are explained in detail. The book begins by introducing the main ideas and concepts of visual analytics and explaining why it should be considered an essential part of data science methodology and practices. It then describes the general principles underlying the visual analytics approaches, including those on appropriate visual representation, the use of interactive techniques, and classes of computational methods. It continues with discussing how to use visualisations for getting aware of data properties that need to be taken into account and for detecting possible data quality issues that may impair the analysis. The second part of the book describes visual analytics methods and workflows, organised by various data types including multidimensional data, data with spatial and temporal components, data describing binary relationships, texts, images and video. For each data type, the specific properties and issues are explained, the relevant analysis tasks are discussed, and appropriate methods and procedures are introduced. The focus here is not on the micro-level details of how the methods work, but on how the methods can be used and how they can be applied to data. The limitations of the methods are also discussed and possible pitfalls are identified. The textbook is intended for students in data science and, more generally, anyone doing or planning to do practical data analysis. It includes numerous examples demonstrating how visual analytics techniques are used and how they can help analysts to understand the properties of data, gain insights into the subject reflected in the data, and build good models that can be trusted. Based on several years of teaching related courses at the City, University of London, the University of Bonn and TU Munich, as well as industry training at the Fraunhofer Institute IAIS and numerous summer schools, the main content is complemented by sample datasets and detailed, illustrated descriptions of exercises to practice applying visual analytics methods and workflows.

Due to rapid advances in hardware and software technologies, network infrastructure and data have become increasingly complex, requiring efforts to more effectively comprehend and analyze network topologies and information systems. Innovative Approaches of Data Visualization and Visual Analytics evaluates the latest trends and developments in force-based data visualization techniques, addressing issues in the design, development, evaluation, and application of algorithms and network topologies. This book will assist professionals and researchers working in the fields of data analysis and information science, as well as students in computer science and computer engineering, in developing increasingly effective methods of knowledge creation, management, and preservation.

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

The Book has been written completely as per AICTE recommended syllabus on "Data Sciences". SALIENT FEATURES OF THE BOOK: Explains how data is collected, managed and stored for data science. With complete courseware for understand the key concepts in data science including their real-world applications and the toolkit used by data scientists. Implement data collection and management. Provided with state of the arts subjectwise. With all required tutorials on R, Python and Bokeh, Anaconda, IBM SPSS-21 and Matplotlib.

Data Science and Data Analytics explores the foundational concepts, methodologies, and tools that drive data-driven decision-making in various industries. This book provides a comprehensive overview of data collection, processing, analysis, and visualization techniques, emphasizing practical applications and real-world case studies. Readers will gain insights into statistical methods, machine learning algorithms, and the importance of data ethics, equipping them with the knowledge to harness the power of data for informed decision-making and strategic planning in an increasingly data-centric world.

Data science addresses the need to extract knowledge and information from data volumes, often from real-time sources in a wide variety of disciplines such as astronomy, bioinformatics, engineering, science, medicine, social science, business, and the humanities. The range and volume of data sources has increased enormously over time, particularly those generating real-time data. This has posed additional challenges for data management and data analysis of the data and effective representation and display. A wide range of application areas are able to benefit from the latest visual tools and facilities. Rapid analysis is needed in areas where immediate decisions need to be made. Such areas include weather forecasting, the stock exchange, and security threats. In areas where the volume of data being produced far exceeds the current capacity to analyze all of it, attention is being focussed how best to address these challenges. Optimum ways of addressing large data sets across a variety of disciplines have led to the formation of national and institutional Data Science Institutes and Centers. Being driven by national priority, they are able to attract support for research and development within their organizations and institutions to bring together interdisciplinary expertise to address a wide variety of problems. Visual computing is a set of tools and methodologies that utilize 2D and 3D images to extract information from data. Such methods include data analysis, simulation, and interactive exploration. These are analyzed and discussed.