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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.
The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then deciding what subsets of data to make available. [ 3 ] Previously, analytics was considered a type of after-the-fact method of forecasting consumer behavior by examining the number of units sold in the last quarter or the last year.
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...
WPS Analytics: S + + + + + + + + + + Product s/w type [Note 1] Descriptive statistics Nonparametric statistics Quality control Survival analysis Data processing Base stat. [Note 2] Normality tests [Note 3] CTA [Note 4] Nonparametric comparison, ANOVA: Cluster analysis Discriminant analysis BDP [Note 5] Ext. [Note 6]
An alternative data set can be compiled from various sources such as financial transactions, sensors, mobile devices, satellites, public records, and the internet. [3] [8] [9] [10] Alternative data can be compared with data that is traditionally used by investment companies such as investor presentations, SEC filings, and press releases. [11]
Computational finance is a branch of applied computer science that deals with problems of practical interest in finance. [1] Some slightly different definitions are the study of data and algorithms currently used in finance [ 2 ] and the mathematics of computer programs that realize financial models or systems .
The term data processing has mostly been subsumed by the more general term information technology (IT). [5] The older term "data processing" is suggestive of older technologies. For example, in 1996 the Data Processing Management Association (DPMA) changed its name to the Association of Information Technology Professionals. Nevertheless, the ...