Data science and big data

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Reference no: EM132695649

While this weeks topic highlighted the uncertainty of Big Data, the author identified the following as areas for future research.

Additional study must be performed on the interactions between each big data characteristic, as they do not exist separately but naturally interact in the real world.

The scalability and efficacy of existing analytics techniques being applied to big data must be empirically examined.

New techniques and algorithms must be developed in ML and NLP to handle the real-time needs for decisions made based on enormous amounts of data.

More work is necessary on how to efficiently model uncertainty in ML and NLP, as well as how to represent uncertainty resulting from big data analytics.

Since the CI algorithms are able to find an approximate solution within a reasonable time, they have been used to tackle ML problems and uncertainty challenges in data analytics and process in recent years.

Reference no: EM132695649

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