Big Data Analytics Coursework

Big Data Analytics is listed as IS 592 in catalog.  Because it is a higher level class building on material from IS 584 (Database Management Systems), I’m looking forward to exploring data analytics and data science in the formal classroom setting.

Data life cycle image with steps analyze plan collect assure describe preserve discover integrate analyze.

Steps in data life cycle include “Analyze”

I’m grateful to have the opportunity to study this topic at the School of Information Sciences.  It is one of a handful of courses in the overall course catalog that are closely aligned with my career goals to work as a professional data manager.  My intuition is that individuals who are trained to manage data must have an extensive knowledge of the data life cycle to effectively manage data across the spectrum of its life.  Note that “Analyze” is one of the last steps.  Even for strictly traditional views of curation, an archivist who is not familiar with flow of information in the “Analyze” step is not well positioned to receive and curate research output that is increasingly data intensive.

From the first lecture, a quote caught my attention:

We project a need for 1.5 million additional managers and analysts in the United States who can ask the right questions and consume the results of the analysis of Big Data effectively.

This is a huge and growing field.  What caught my eye is the growing number of specialty schools to turn out students with the skills to analyze data.  Even at UT-Knoxville, there are three “data science” courses – one in Computer Science, one in Statistics, and one in Information Sciences.  These are three separate colleges (Engineering, Arts & Sciences, and Communication and Information). Established information studies programs are likely seats for new data science programs and curriculum.  Berkely’s school of information just stood up a new program to support a Masters in Information and Data Science.  Other short “boot camps” are offered, although I’m not sure if those programs will produce data “scientists” or just data “analysts” – the key from the quote above is “ask the right questions.” Are our new data science programs able to impart the skill to ask questions using the scientific method?

From the first lecture, here are skills that data analysts need:

  1. Database
  2. Data mining
  3. Statistical applications
  4. Predictive analytics
  5. Business intelligence
  6. Data modeling and data visualization
  7. Meta cognitive strategies
  8. Interpersonal

Coursework for IS 592 will be collected at the following URL:


About Tanner Jessel

I am a recent M.S. in Information Science graduate from the University of Tennessee School of Information Science. I was formerly a graduate research assistant funded by DataONE (Data Observation Network for Earth). Prior, I worked for four years as a content lead and biodiversity scientist with the U.S. Geological Survey's Biodiversity Informatics Program. Building on my work experience in biodiversity and environmental informatics, my work with DataONE focused on exploring the nature of scientific collaborations necessary for scientific inquiry. I also conducted research concerning user experience and usability, and assisted in development of member nodes with an emphasis on spatial data and infrastructure. I assisted with research designed to understand sociocultural issues within collaborative research communities. Through August 1, 2014, I was based at the Center for Information and Communication Studies at the University of Tennessee School of Information Science in Knoxville, Tennessee.

Posted on January 9, 2014, in Big Data Analytics, Coursework and tagged , , , , . Bookmark the permalink. Leave a comment.

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