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2. IBM SkillsBuild - Wikipedia

   en.wikipedia.org/wiki/IBM_SkillsBuild

   IBM SkillsBuild offers 1,000 courses in 20 languages on artificial intelligence, cybersecurity, data analysis, cloud computing, sustainability, and many other technical disciplines — as well as in workplace skills such as Design Thinking. Participants can earn IBM-branded digital credentials that are recognized by the market.

3. SPSS Modeler - Wikipedia

   en.wikipedia.org/wiki/SPSS_Modeler

   These two bundles are called "editions" by IBM: SPSS Modeler Professional: used for structured data, such as databases, mainframe data systems, flat files or BI systems; SPSS Modeler Premium: Includes all the features of Modeler Professional, with the addition of: – Text analytics. Both editions are available in desktop and server configurations.

4. Data science - Wikipedia

   en.wikipedia.org/wiki/Data_science

   Data analysis focuses on extracting insights and drawing conclusions from structured data, while data science involves a more comprehensive approach that combines statistical analysis, computational methods, and machine learning to extract insights, build predictive models, and drive data-driven decision-making. Both fields use data to ...

5. IBM Watson Studio - Wikipedia

   en.wikipedia.org/wiki/IBM_Watson_Studio

   IBM announced the launch of Data Science Experience at the Spark Summit 2016 in San Francisco. IBM invested $300 million in efforts to make Spark the analytics operating system for all of the company's big data efforts. [3] In June 2017, Hortonworks and IBM announced their partnership to collaborate on IBM's Data Science Experience. Hortonworks ...

6. Analytics - Wikipedia

   en.wikipedia.org/wiki/Analytics

   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.

7. Cross-industry standard process for data mining - Wikipedia

   en.wikipedia.org/wiki/Cross-industry_standard...

   A review and critique of data mining process models in 2009 called the CRISP-DM the "de facto standard for developing data mining and knowledge discovery projects." [16] Other reviews of CRISP-DM and data mining process models include Kurgan and Musilek's 2006 review, [8] and Azevedo and Santos' 2008 comparison of CRISP-DM and SEMMA. [9]