Course: Data Science & Big Data Analy

LATE SUBMISSION WILL NOT BE ACCEPTED BY PROF.

Due Date – 2 days

Discussion Question:  Big Data

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.

Prof. Guidelines 

Your paper should meet these requirements: 

• Be approximately four to six 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.

Reading Assignments 

Marcu, D., & Danubianu, M. (2019). Learning Analytics or Educational Data Mining? This is the Question. BRAIN: Broad Research in Artificial Intelligence & Neuroscience, 10, 1–14. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=a9h&AN=139367236&site=eds-live

Hariri,  R.H., Fredericks, E.M. & Bowers, K.M. (2019). Uncertainty in big  data analytics: survey, opportunities, and challenges. Journal of Big Data, 6: 44. https://doi.org/10.1186/s40537-019-0206-3

Books and Resources 

Required Text

Eyupoglu, C. (2019). Big Data in Cloud Computing and Internet of Things. 2019     3rd International Symposium on Multidisciplinary Studies and    Innovative  Technologies (ISMSIT), Multidisciplinary Studies and    Innovative  Technologies (ISMSIT), 2019 3rd International Symposium On, 1–5. https://doi.org/10.1109/ISMSIT.2019.8932815

L. Zhao, Y. Huang, Y. Wang and J. Liu, “Analysis on the Demand of Top     Talent Introduction in Big Data and Cloud Computing Field in China     Based on 3-F Method,” 2017 Portland International Conference on     Management of Engineering and Technology (PICMET), Portland, OR,   2017,    pp. 1-3. https://doi.org/10.23919/PICMET.2017.8125463

Saiki, S., Fukuyasu, N., Ichikawa, K., Kanda, T., Nakamura, M.,     Matsumoto, S., Yoshida, S., & Kusumoto, S. (2018). A Study of     Practical Education Program on AI, Big Data, and Cloud Computing    through  Development of Automatic Ordering System. 2018 IEEE    International  Conference on Big Data, Cloud Computing, Data Science    & Engineering  (BCD), Big Data, Cloud Computing, Data Science  &   Engineering (BCD),  2018 IEEE International Conference on, BCD, 31–36. https://doi.org/10.1109/BCD2018.2018.00013

Psomakelis, E., Aisopos, F., Litke, A., Tserpes, K., Kardara, M., & Campo, P. M. (2016). Big     IoT and social networking data for smart cities: Algorithmic     improvements on Big Data Analysis in the context of RADICAL city     applications.

Liao, C.-H., & Chen, M.-Y. (2019). Building social computing     system in big data: From the perspective of social network analysis. Computers in Human Behavior, 101, 457–465. https://doi.org/10.1016/j.chb.2018.09.040

“APA Format” 

https://academicwriter.apa.org/6/ 

“NO PLAGIARISM” 

Plagiarism includes copying and pasting material     from the internet into assignments without properly citing the source     of the material.