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To ensure robust analysis, dataanalytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more. What are the four types of dataanalytics? Dataanalytics and datascience are closely related.
There are other dimensions of analytics that tend to focus on hindsight for business reporting and causal analysis – these are descriptive and diagnosticanalytics, respectively, which are primarily reactive applications, mostly explanatory and investigatory, not necessarily actionable. Pay attention!
Though you may encounter the terms “datascience” and “dataanalytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, dataanalytics is the act of examining datasets to extract value and find answers to specific questions.
Artificial Intelligence Analytics. AI can be applies to all 3 major types of analytics: DescriptiveAnalytics: The entire journey of the descriptive and diagnosticanalytics process includes data extraction, data aggregation and data mining; 3 applications where AI is widely used to reduce costs, and eliminate complex actions.
Data analysts leverage four key types of analytics in their work: Prescriptive analytics: Advising on optimal actions in specific scenarios. Diagnosticanalytics: Uncovering the reasons behind specific occurrences through pattern analysis.
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