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Total Size:
54.8 MB
Info Hash:
A7DFA76A9A78EE1F2CD9AFB07C9913AA35FB1B22
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Added:
April 22, 2026, 3:51 p.m.
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(Last updated: April 22, 2026, 3:51 p.m.)
| File | Size |
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| ['Vadivel A. Interactive and Dynamic Dashboard. Design Principles 2025.pdf'] | 0 bytes |
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54.8 MB
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2026-04-22
| Uploaded by andryold1 | Size 54.8 MB | Health [ 39 /32 ] | Added 2026-04-22 |
NOTE
SOURCE: Vadivel A. Interactive and Dynamic Dashboard. Design Principles 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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 Introd 1 Bibliometric analysis on visual data analysis and dynamic dashboard tools: A literature review 2 Visual data analysis and inference through dimensionality reduction techniques 3 Visual data analysis of temperature, ground water level, precipitation for climate-driven socio-economic prediction 4 AI-based online interview bot with an interactive dashboard 5 Visualizing food quality and safety: A dynamic dashboard approach with near-infrared imaging and machine learning 6 Interactive dashboard and 3D visualization using t-SNE dimensionality reduction technique 7 Dynamic dashboard creation for sales trends and optimize pricing strategies 13 8 Scaling up the business with the aid of power query tool 9 Interactive visualization techniques for thermal imaging analysis in ophthalmology: Comparative insights and future directions 10 Mind scan: Dynamic brain cancer detection dashboard with MRI imaging 11 Interactive and dynamic stock market dashboard 12 Performance analysis of hierarchical clustering and high-dimensional clustering algorithms on network IDS benchmark datasets using interactive dynamic dashboard 13 Breaking boundaries: The next frontier in skin cancer diagnosis combining transfer learning and multi-scale deep learning 14 Nourish net: Machine learning innovations in food recognition and calorie monitoring 15 Comprehensive study of coral reef assessment
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