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  2. Big data - Wikipedia

    en.wikipedia.org/wiki/Big_data

    Big data uses mathematical analysis, optimization, inductive statistics, and concepts from nonlinear system identification [33] to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density [34] to reveal relationships and dependencies, or to perform predictions of outcomes and ...

  3. Data science - Wikipedia

    en.wikipedia.org/wiki/Data_science

    Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. [6]

  4. Data management - Wikipedia

    en.wikipedia.org/wiki/Data_management

    While there are numerous analysis tools in the market, Big Data analytics is the most common and advanced technology that has led to the following hypothesis: Data analytic tools used to analyze data collected from numerous data sources determine the quality and reliability of data analysis.

  5. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analytics (or customers, who will use the finished product of the analysis). [ 14 ] [ 15 ] The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of ...

  6. Analytics - Wikipedia

    en.wikipedia.org/wiki/Analytics

    Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.

  7. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    KNIME: The Konstanz Information Miner, a user-friendly and comprehensive data analytics framework. Massive Online Analysis (MOA): a real-time big data stream mining with concept drift tool in the Java programming language. MEPX: cross-platform tool for regression and classification problems based on a Genetic Programming variant.

  8. Business intelligence - Wikipedia

    en.wikipedia.org/wiki/Business_intelligence

    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 ...

  9. Big data maturity model - Wikipedia

    en.wikipedia.org/wiki/Big_Data_Maturity_Model

    The TDWI big data maturity model is a model in the current big data maturity area and therefore consists of a significant body of knowledge. [6] Maturity stages. The different stages of maturity in the TDWI BDMM can be summarized as follows: Stage 1: Nascent. The nascent stage as a pre–big data environment. During this stage:

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