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Includes: relationships in data: when and how to use data visualisation what makes good – and bad – data visualisations quick wins and common mistakes Data visualisation formats Identify correlations, clusters or patterns and turn them into infographics.
It does this by looking at the causal relationships in data.
We are aiming for an automated approach to explore and reveal hidden, unknown relationships in data.
Recovery of causal relationships in data is an essential part of scholarly inquiry in the social sciences.
Social physics leads towards finding universal relationships in data on the basis of well-understood and statistically robust methodologies.
Data science uses algorithms to collect and analyze up to thousands, millions, or billions of rows of columns, automatically discovering new relationships in data.
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PathCase visualization tools visualize metabolic data, relationships in the data, as well as analysis results of the data via multiple variations of a JAVA applet.
In addition, for feedback controller design, a data-based stabilizability condition is developed by using a geometrical relationship in data spaces.
Exploratory data analysis was used to detect relationships in the data and evaluate data dependence.
The integration of data and tools in the PDDB helps scientists explore relationships in the data efficiently and collectively evaluate the data.
However, the complex structures and relationships inherent in data in non-standard applications are not accommodated well by OLAP systems.
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