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  2. Data analysis for fraud detection - Wikipedia

    en.wikipedia.org/wiki/Data_analysis_for_fraud...

    The main steps in forensic analytics are data collection, data preparation, data analysis, and reporting. For example, forensic analytics may be used to review an employee's purchasing card activity to assess whether any of the purchases were diverted or divertible for personal use.

  3. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    Predictive analytics is a set of business intelligence (BI) technologies that uncovers relationships and patterns within large volumes of data that can be used to predict behavior and events. Unlike other BI technologies, predictive analytics is forward-looking, using past events to anticipate the future. [3]

  4. JMP (statistical software) - Wikipedia

    en.wikipedia.org/wiki/JMP_(statistical_software)

    JMP software is partly focused on exploratory data analysis and visualization. It is designed for users to investigate data to learn something unexpected, as opposed to confirming a hypothesis. [ 5 ] [ 26 ] [ 43 ] JMP links statistical data to graphics representing them, so users can drill down or up to explore the data and various visual ...

  5. SAS (software) - Wikipedia

    en.wikipedia.org/wiki/SAS_(software)

    SAS (previously "Statistical Analysis System") [1] is a statistical software suite developed by SAS Institute for data management, advanced analytics, multivariate analysis, business intelligence, criminal investigation, [2] and predictive analytics. SAS' analytical software is built upon artificial intelligence and utilizes machine learning ...

  6. Link analysis - Wikipedia

    en.wikipedia.org/wiki/Link_analysis

    Data analysis techniques are required to make effective and efficient use of the data. Palshikar classifies data analysis techniques into two categories – (statistical models, time-series analysis, clustering and classification, matching algorithms to detect anomalies) and artificial intelligence (AI) techniques (data mining, expert systems ...

  7. Augmented Analytics - Wikipedia

    en.wikipedia.org/wiki/Augmented_Analytics

    Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. [1] The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper. [1] [2]

  8. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    It also is a buzzword [7] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence.

  9. NodeXL - Wikipedia

    en.wikipedia.org/wiki/NodeXL

    NodeXL Pro imports UCINet and GraphML files, as well as Excel spreadsheets containing edge lists or adjacency matrices, into NodeXL workbooks. NodeXL Pro also allows for the quick collection of social media data via a set of import tools which can collect network data from e-mail, Twitter, YouTube, and Flickr.