Visual Analytics For Data Scientists
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Visual Analytics for Data Scientists
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.
Big Data and Visual Analytics
This book provides users with cutting edge methods and technologies in the area of big data and visual analytics, as well as an insight to the big data and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics. Each chapter covers specific topics related to big data and data analytics as virtual data machine, security of big data, big data applications, high performance computing cluster, and big data implementation techniques. Every chapter includes a description of an unique contribution to the area of big data and visual analytics. This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference.
DATA SCIENCE AND STRUCTURAL ANALYSIS
Author: Dr. Raja Sarath Kumar Boddu
language: en
Publisher: Xoffencerpublication
Release Date:
Computers have become an integral part of our daily life in recent times. They have enormously impacted our personal, professional, as well as social lives. Considering the increasing demand of computers in society, schools, colleges, and universities have included computer education in their curriculum, to help students become skilled in programming and developing applications which can be used to solve various business, scientific, and social problems. This study focuses on a specific kind of research known as structured data analysis. This book is all about models and hypotheses, whether they are large or little, backed by evidence or not. Making educated business choices based on solid theory and empirical evidence is of the utmost importance. The theory and application of data analytics are both covered in this book. A skilled data scientist will have little trouble working with massive datasets, either from the technical standpoint of databases or by developing algorithms to extract the most value from the data. The analytics project team may flaunt their hard work and other groups can put the models to use in production with the help of the code and technical documentation that was produced as part of the project deliverables. Here we see how simple visuals may greatly enhance the effectiveness of a presentation or data visualization. After some time had passed, however, a sizable quantity of information about Covid-19 individuals' symptoms, severe medical issues, hospitalizations, etc. was made public. This data was used to develop an algorithm that forecasts how severely unwell individuals with the Covid-19 mutation would be and how likely they are to live.