ITS 836 Cumberlands Week 4 Interactions Between Big Data Characteristics Paper

There is much discussion regarding Data Analytics and Data Mining. Sometimes these terms are used synonymously but there is a difference. What is the difference between Data Analytics vs Data Mining? Please provide an example of how each is used. Also explain how you may use data analytics and data mining in a future career. Lastly, be sure to utilize at least one scholarly source from either the UC library or Google Scholar.

Please make your initial post and two response posts substantive. A substantive post will do at least two of the following:

• Ask an interesting, thoughtful question pertaining to the topic
• Answer a question (in detail) posted by another student or the instructor
• Provide extensive additional information on the topic
• Explain, define, or analyze the topic in detail
• Share an applicable personal experience
• Provide an outside source (for example, an article from the UC Library) that applies to the topic, along with additional information about the topic or the source (please cite properly in APA)
• Make an argument concerning the topic.

At least one scholarly source should be used in the initial discussion thread. Be sure to use information from your readings and other sources from the UC Library. Use proper citations and references in your post.

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While this weeks topic highlighted the uncertainty of Big Data, the author identified the following as areas for future research. Pick one of the following for your Research paper:

• 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.

Your paper should meet these requirements:

• Be approximately four to eight pages in length, not including the required cover page and reference page.
• Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
• Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources.
• Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.