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Waterfall charts can be used for various types of quantitative analysis, ranging from inventory analysis to performance analysis. [4] Waterfall charts are also commonly used in financial analysis to display how a net value is arrived at through gains and losses over time or between actual and budgeted amounts. Changes in cash flows or income ...
Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope ...
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Analytics is the systematic computational analysis of data or statistics. [1] It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. [2] Analytics also entails applying data patterns toward effective decision-making.
Statistical finance [1] is the application of econophysics [2] to financial markets. Instead of the normative roots of finance , it uses a positivist framework. It includes exemplars from statistical physics with an emphasis on emergent or collective properties of financial markets.
A data chart is a type of diagram or graph, that organizes and represents a set of numerical or qualitative data. Maps that are adorned with extra information ( map surround ) for a specific purpose are often known as charts, such as a nautical chart or aeronautical chart , typically spread over several map sheets .
Collecting data from numerous sources and analyzing it using different data analysis tools has its advantages, including overcoming the risk of method bias; using data from different sources and analyzing it using multiple analysis methods guarantees businesses and organizations robust and reliable findings they can use in decision making.
Data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis Techniques to support production, presentation, and dissemination of the results of an analysis to communicate information in the appropriate context to a variety of audiences.