A little side note before I begin this blog: I made a typing mistake in my previous blog. I wrote that NORA stood for "Non-Observational Relationship Awareness." I meant to write that it stood for "Non-Obvious Relationship Awareness."
Today, I began with the basics of how NORA works. However, I quickly transitioned to big data, predictive analytics, and statistics. I needed to develop a strong understanding of data analysis before I jump to how it is applied to NORA. I do not want to get ahead of myself.
Even though the sources I reviewed so far focused on business analytics, the basic idea of big data and predictive analytics remains: large amounts of randomized data is processed and a conclusion is made accordingly. One of the websites provided a list of programs and languages that are used in predictive analytics. A couple of the programming languages are R and Python. I don't know which language I will use for my senior thesis. R is the more popular choice among data scientists, but its language is more confusing to me. It is different from what I am used to. At the moment, I am leaning more towards Python because the syntax is simpler, and it is an effective language for data structures (to hold data).
Data mining is the backbone of NORA programs and big data analytics. I studied the types of statistical methods used in data mining. Data mining is a relatively new discipline, so the information about it is a little vague.
Today was a big of a slow day in terms of research, but the basics are just as important as its applications.
Sources Used:
http://www.opentextbooks.org.hk/ditatopic/18595
https://www.slideshare.net/neymarsabin/non-obvious-relationship-awareness-nora
http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics
https://www.predictiveanalyticstoday.com/what-is-predictive-analytics/
http://statweb.stanford.edu/~jhf/ftp/dm-stat.pdf
Nice work.
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