2025
In the era of big data, the ability to derive meaningful insights from complex datasets is critical across industries. Data visualization and analytics serve as fundamental tools for transforming raw data into actionable intelligence. This chapter explores the principles, techniques, and tools that drive effective data analysis and communication. It begins with an overview of different data types and the foundational principles of clear and accurate visualization. Various methods of data representation—ranging from basic charts to interactive dashboards—are examined alongside popular tools such as Tableau, Power BI, Python libraries, and R. The chapter also introduces core analytics concepts including descriptive, diagnostic, predictive, and prescriptive approaches, emphasizing the importance of data preparation, statistical techniques, and machine learning in driving decisions. Ethical considerations, such as avoiding misleading visuals and maintaining data privacy, are also discussed. Through real-world applications and case studies, readers will gain practical knowledge on how to integrate data visualization with analytics to uncover patterns, forecast trends, and support data-driven decision-making in a visually compelling and ethically sound manner.
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Dr.Alkawati Magadum, Prof. Monica Goud · Zenodo (CERN European Organization for Nuclear Research) · 2024
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Kerry Nenn · The major gifts report · 2025
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Kaouk, Ted, Kaouk, Ted · AgEcon Search (University of Minnesota, USA) · 2021
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Mohammad Atif · International Journal of Engineering in Computer Science · 2022
The intricacy of information is one of the most important properties of data scalability. In the regarding data unification and the solution of big data challenges, heterogeneous data plays an important role. As a result of the vast amount of computation and storage guidelines recommended by big-scale databases, each of them are vital, but they are also difficult to view and analyse. Digital extraction in the data era, in which data increases at an exponential rate, is a huge challenge since the human mind cannot process it in its entirety. With the help of some earlier research, this study examines and offers data visualisation and the description of Heterogeneous Distributed Storage, as well as the issues associated with these technologies. Apart from that, the findings of the examined research papers are contrasted, and the fundamental change in the realm of huge data presentation brought about by virtual reality is explored.
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Yang Yang · 2022
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Mustafa Ozkaynak, Diane J. Skiba · Health informatics · 2022
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Dekker, Harrison · Journal of Media Literacy Education · 2022
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David Auber, Nikos Bikakis, Panos K. Chrysanthis et al. · Big Data Research · 2024
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Paolo Buono, Rosa Lanzilotti · Human-Computer Interaction · 2024
The diffusion of mobile and pervasive technology continuously produces incremental, complex and different datasets that should also be queried to retrieve the information people demand. The term “Big Data” includes such a context, characterized by the volume, velocity, variety, and veracity of data, which require the definition of innovative approaches for allowing people to better understand and extract meaningful insights from data. Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. This chapter discusses big data visualization and visual analytics (Thomas and Cook, 2005), as research fields that are positioned at the intersection of data science, computer science, and design and explores the role of visualization to enhance the analysis and understanding of data. A primary goal of the analysis is to obtain the required information from big dataset, but understanding the data is of greater value as it evidences the efficacy of the presentation of the results of the analysis to the users. Visual representations of information have a distinct capacity to reveal patterns, trends, and outliers that might otherwise remain hidden within the enormity of datasets. The visualization of information is a process, whose activities span from raw data gathering to information presentation in a way that empowers users to make informed decisions; its goal is to align the presentation of the information with the user’s mental model (Card et al., 1999).
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Ivo D. Dinov · The Springer series in applied machine learning · 2023
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City University of Seattle · National University System Repository (National University System) · 2021
This course begins with the application of data-oriented analysis techniques for business intelligence and organizational decision-making.Students will examine a range of tools to enhance the managerial and leadership decision-making processes such as pivot tables, descriptive statistics, statistical process control for business process improvement, and data models to predict future trends.Then students will learn the best practices for communicating the results of their data analysis.They will analyze the questions the organization has asked, collect and analyze relevant data sets, and then present the results in a way that addresses the original questions.
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Arcila Calderón, Carlos, Barredo Ibáñez, Daniel, Castro, Cosette · Repositorio Institucional E-DocUR (Universidad Del Rosario) · 2017
Twitter se ha convertido en un importante canal de comunicación por su facilidad de uso y la rapidez con que circulan los contenidos por medio del llamado microblogging. Este libro reúne trabajos de investigadores iberoamericanos en torno a los diferentes tipos de contenidos publicados en esta plataforma, que van desde datos identitarios hasta noticias y mensajes de activismo. El trabajo con grandes cantidades de datos requiere de herramientas para el análisis y la visualización, por lo que esta obra se enfoca en diversas estrategias que profesionales de la comunicación e investigadores sociales pueden usar para tal fin.
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Stelzer, Carson; Wang, Uen-Tao; Danford, Forest; Wong, Andrew; Armstrong, Emily; Fackrell, Katherine; Lin, Nathan; Sullivan, Patrick Ryan · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2016
Abstract not provided.
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Allen, Jeff, Greenberg, Julia, St-Louis, Evelyne et al. · Zenodo (CERN European Organization for Nuclear Research) · 2025
An online textbook, written and created by the School of Cities at the University of Toronto. It is a combination of learning materials on data analytics, data visualization, and narrative techniques to make complex urban trends understandable and engaging for specific audiences, such as policymakers, funders, or community members. There are three core modules, each composed of several chapters and hands-on tutorials. Urban data storytelling: Craft compelling narratives about cities, in order to communicate research and key insights, inform policy-making, build public will, or advocate for change. Urban data analysis: Find, process, and analyze data via Python, spreadsheet software, and GIS aimed at deriving key findings and answering research questions. Urban data visualization: Learn a variety of strategies, methods, and practical tips for effective visual communication of urban data via a range of maps and charts.
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Allen, Jeff, Greenberg, Julia, St-Louis, Evelyne et al. · Zenodo (CERN European Organization for Nuclear Research) · 2025
An online textbook, written and created by the School of Cities at the University of Toronto. It is a combination of learning materials on data analytics, data visualization, and narrative techniques to make complex urban trends understandable and engaging for specific audiences, such as policymakers, funders, or community members. There are three core modules, each composed of several chapters and hands-on tutorials. Urban data storytelling: Craft compelling narratives about cities, in order to communicate research and key insights, inform policy-making, build public will, or advocate for change. Urban data analysis: Find, process, and analyze data via Python, spreadsheet software, and GIS aimed at deriving key findings and answering research questions. Urban data visualization: Learn a variety of strategies, methods, and practical tips for effective visual communication of urban data via a range of maps and charts.
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Paolo Buono, Rosa Lanzilotti · 2024
Modern life is characterized by the high presence of pervasive technology that produces data at rates and quantity that require not only computational power but also sophisticated techniques, tools, and user interface design to facilitate people to access the data they need. This data complexity falls under the term “big data”, signifying the need for intricate solutions to address intricate problems. In a world dominated by smartphones, people are used to accessing information visually. Data visualization emerges as a potent means of rapidly and effectively conveying even the most intricate information. The research field of visual analytics addresses complex scenarios through the combination of automatic approaches with visualizations, which may help the user interact with complex systems. This chapter discusses the realms of big data visualization and visual analytics, situated at the crossroads of data science, computer science, and design. It examines the pivotal role played by visualization and visual analytics in enhancing the comprehension of data within the context of big data.
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Harishchander Anandaram, Rakheeba Taseen, Haseeba Yaseen et al. · Advances in web technologies and engineering book series · 2025
Data visualisation allows for the visual representation of data and information. Trends and changes in data may be seen with the use of data visualisation tools and visual data analysis using visual elements. The basic goal of data visualisation is to help people better comprehend and examine data—that is, to comprehend the outcomes of their processing—by transforming it into a visual environment. Humans have the quickest cognitive pathway in the visual canal. This is why visual data is more appealing than text or audio data. It is common for quick data processing, analysis, and presentation to become more important in daily life in this era of digital data overproduction. Processing tools and visual data analysis undergo continual upgrading as a result of the two-way communication between developers, managers, and users. With that in mind, this chapter will examine and contrast the efficiency of the various visual data analysis and visualisation techniques available today.
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Theni Kammavar Sangam College of Arts and Science · Zenodo (CERN European Organization for Nuclear Research) · 2025
Abstract This paper presents a critical review of analytics and visualization technologies for big data and proposes future directions to address the limitations of current approaches. Traditional machine learning and datamining algorithms generally operate on predefined scales of aggregation, which are inadequate for handling the dynamic and heterogeneous nature of large datasets. To overcome this, we identify a novel architecture based on a flexible multiresolution hypothesis space that enables advanced model discovery, including semisupervised and unsupervised anomaly detection, complex relationship analysis, and visualization of intricate processes. The continual growth of big data emphasizes the need for efficient analytical and visualization tools that leverage human cognitive and visual capabilities for intuitive data exploration. However, existing visualization systems often lack scalability, interactivity, and user assistance. Through a systematic literature review, six key requirements for visualization tools are identified: dimensionality reduction, data reduction, scalability and readability, interactivity, fast result retrieval, and user assistance. Furthermore, the study explores data visualization and analytics within the context of mining digitalization, highlighting data fusion, digital twin development, and machine learning-oriented fusion applications as emerging areas. The paper also examines the Data Analytics Framework (DAF) developed by the Italian government as a case study demonstrating open data integration and visualization for public sector efficiency. Overall, this review provides a comprehensive understanding of current challenges and future research directions in big data visualization and analytics, emphasizing the need for flexible, interactive, and intelligent systems that can adapt to evolving data environments.
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S. Karthika, D. Varshini, M. Vidhya Bharathi · Zenodo (CERN European Organization for Nuclear Research) · 2025
This paper presents a critical review of analytics and visualization technologies for big data and proposes future directions to address the limitations of current approaches. Traditional machine learning and data-mining algorithms generally operate on predefined scales of aggregation, which are inadequate for handling the dynamic and heterogeneous nature of large datasets. To overcome this, we identify a novel architecture based on a flexible multiresolution hypothesis space that enables advanced model discovery, including semi-supervised and unsupervised anomaly detection, complex relationship analysis, and visualization of intricate processes. The continual growth of big data emphasizes the need for efficient analytical and visualization tools that leverage human cognitive and visual capabilities for intuitive data exploration. However, existing visualization systems often lack scalability, interactivity, and user assistance. Through a systematic literature review, six key requirements for visualization tools are identified: dimensionality reduction, data reduction, scalability and readability, interactivity, fast result retrieval, and user assistance. Furthermore, the study explores data visualization and analytics within the context of mining digitalization, highlighting data fusion, digital twin development, and machine learning-oriented fusion applications as emerging areas. The paper also examines the Data Analytics Framework (DAF) developed by the Italian government as a case study demonstrating open data integration and visualization for public sector efficiency. Overall, this review provides a comprehensive understanding of current challenges and future research directions in big data visualization and analytics, emphasizing the need for flexible, interactive, and intelligent systems that can adapt to evolving data environments.
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