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2. Big data - Wikipedia

   en.wikipedia.org/wiki/Big_data

   Big data "size" is a constantly moving target; as of 2012 ranging from a few dozen terabytes to many zettabytes of data. [26] Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data-sets that are diverse, complex, and of a massive scale. [27]

3. Exploratory data analysis - Wikipedia

   en.wikipedia.org/wiki/Exploratory_data_analysis

   Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." [3]

4. Cloud analytics tools - Wikipedia

   en.wikipedia.org/wiki/Cloud_Analytics

   Cloud analytics is term for a set of technological and analytical tools and techniques specifically designed to help clients extract information from massive data. [ 2 ] Cloud analytics is designed to make official statistical data readily categorized and available via the users web browser .

5. Data mining - Wikipedia

   en.wikipedia.org/wiki/Data_mining

   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. mlpack: a collection of ready-to-use machine learning algorithms written in the C++ language.

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. Predictive analytics - Wikipedia

   en.wikipedia.org/wiki/Predictive_analytics

   Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.