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The open version of IBM SkillsBuild is an online platform which offers over 1,000 courses in 20 languages on artificial intelligence, cybersecurity, data analysis, cloud computing and many other technical disciplines — as well as in workplace skills such as Design Thinking.
Its mission is to strengthen the cybersecurity workforce and awareness of cybersecurity and cyberspace through accessible education. [3] With over 6,000 cyber security training courses, career pathway tools, and up-to-date coverage on cybersecurity events and news, NICCS aims to empower current and future generations of cybersecurity professionals.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
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.
Unlike other BI technologies, predictive analytics is forward-looking, using past events to anticipate the future. [3] 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 ...
SAS Institute (or SAS, pronounced "sass") is an American multinational developer of analytics and artificial intelligence software based in Cary, North Carolina.SAS develops and markets a suite of analytics software (also called SAS), which helps access, manage, analyze and report on data to aid in decision-making.
Anomaly detection finds application in many domains including cybersecurity, medicine, machine vision, statistics, neuroscience, law enforcement and financial fraud to name only a few. Anomalies were initially searched for clear rejection or omission from the data to aid statistical analysis, for example to compute the mean or standard deviation.
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."