Название: Interactive and Dynamic Dashboard: Design PrinciplesАвтор: A. Vadivel, K. Meena, P. Sumathy, Henry Selvaraj, P. Shanmugavadivu
Издательство: CRC Press
Год: 2025
Страниц: 302
Язык: английский
Формат: pdf (true)
Размер: 54.8 MB
The text comprehensively discusses the representation of visual data and design principles of interactive and dynamic dashboards. It further covers the theoretical concept of inference and Machine Learning algorithms for making the concepts clear to the reader. The book illustrates important topics such as data testing a parametric hypothesis, data testing a non-parametric hypothesis, exploratory data analysis, outlier detection and interpretation.
A dynamic interactive dashboard is a vital resource in a variety of fields, such as business, finance, healthcare, and education, since it enables users to interact with data in real-time. With the ability to update data in real-time or on a regular basis, dynamic interactive dashboards guarantee that users always have access to the most recent information. This function comes in very handy for tracking indicators that change quickly, such stock prices, website traffic, or social media interaction. These dashboards frequently include interactive visualizations that let users explore data in a more natural and interesting way, like graphs, charts, maps, and gauges. By dragging their cursor over data points, clicking on items to reveal more information, or changing display parameters, users can interact with these visualizations.
A well-liked Python framework called Plotly Dash was created especially for creating interactive dashboards and data visualizations that are accessible online. It makes use of Plotly.js’s capabilities to produce aesthetically pleasing graphs and charts with a great degree of customization and interactivity.
Tableau, a top platform for business intelligence and data visualization, users can build interactive dashboards with a variety of analytics and visualization options. With the help of the well-known data visualization tool Tableau, users can create dynamic, informative dashboards and reports from their data.
This book:
Covers various data analysis tools such as KNIME, RapidMiner, Rstudio, Grafana, and Redash
Discusses the theoretical concept of inference and Machine Learning algorithms for designing dynamic dashboards
Presents statistical modelling techniques with an emphasis on pattern mining, and pattern relationships
Explains the problem of efficient retrieval of similar time series in large databases to enrich the knowledge of the readers to effectively handle various real-time datasets
Illustrates dimensionality reduction techniques such as principal component analysis, linear discriminant analysis, singular value decomposition, and piecewise vector quantized approximation
It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, Computer Science and engineering, and information technology.
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