IS 592 Big Data Analytics

Catalog Description

Introduces the concepts big data and data analytics as an emerging field. To address the opportunities and challenges of big data in academics, businesses, sciences, the Web, etc. To understand the nature of big data analytics and their various contexts. To master basic concepts and process of data analytics. To design analytics initiatives/proposals. To practice data mining techniques and skills. To explore data modeling and visualizing.

Pre-requisite: Database Management Systems (completion of IS584 or equivalent)

Goals/Objectives

  • To survey the needs and importance of data analytics in various contexts
  • To understand the challenges of managing big data
  • To practice data extraction, transformation and load techniques (ETL)
  • To develop algorithms to analyze and model data
  • To design effective ways for communicating results to special usersMethods of Teaching/Learning

    This course is built on knowledge and skills of database management systems. The focus will be on issues challenging organizational decision-making, real world data needs that call for methods of data management, analytics, and modeling to derive new knowledge for better decision making.

    Students are expected to read broadly and to work on real data collected from the real world. This course is managed using Blackboard courseware, which is accessible using your UT NetID and Password at https://bblearn.utk.edu/. The Blackboard Collaborate, a tool hosted
    in Blackboard, will be used for synchronous virtual class sessions; you may attend classes from anywhere in the world. The course materials, assignments, and grades are accessible in Blackboard.

Course Materials Required text:

Jeffrey Stanton with Robert De Graaf (c2013) Version 3: Introduction to Data Science at http://jsresearch.net/

Optional texts:

Bill Franks (2012) Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics (Wiley and SAS Business Series) 337 pages ISBN: 1118208781

Thomas H. Davenport, Jeanne G. Harris (2010) Analytics at Work: Smarter Decisions, Better Results [Hardcover] 240 pages. Harvard Business Review Press

Douglas W. Hubbard (2010) How to Measure Anything: Finding the Value of Intangibles in Business. (2nd edition) 320 pages. Wiley. ISBN-10: 0470539399; ISBN-13: 978- 0470539392

Tasks and Evaluation Criteria
• Attendance & Participation (15%)

Prepared attendance and participation in course activities are important to success in this course. If you have to miss a class for whatever reasons, you are still responsible for the material covered. If you miss a class, you may replay the recording. Blackboard Collaborate keeps track of attendance and replay.

Class activities include presentations and discussion.

• ePortfolio or Journal (10%)

Be a reflective learner! Throughout the semester, you should maintain a learning journal or ePortfolio. Write journal entries to reflect your thoughts, analyze critical incidents, and check milestones.

If you have taken the ePortfolio course, you should continue building your ePortfolio in this course by writing Posts to reflect on your learning and achievements. At the end of the semester, you will write a reflective summary for the course as a Page in your ePortfolio.

If you have not taken the ePortfolio course, you may keep a structured journal with dated entries and write a final reflection piece. You submit the reflection along with selected journal entries in any format accessible to the instructor.

Make your learning and achievements visible through the development of a course ePortfolio. Journal entries or ePortfolio Posts document your learning and professional growth with evidence and through reflection on learning experiences. Both collecting artifacts and reflecting in journal entries are private actions but presenting outcomes and sharing reflective summary are oriented toward a product for public (or your evaluators).

What to write in journal entries (ePortfolio posts)? You do not need to report or log what you have done during the course. You need to focus on significant learning incidents, aha moments, relevant thoughts, analysis and synthesis of important concepts, and milestone checking. Reflection is a higher level of cognitive activity in which you makes sense of what and how you learned. For example, when you encountered a challenging problem, you should reflect on the strategies and the process through which you were, or were not, able to solve the problem. For ePortfolio students, you should classify your journal entries so that they can be easily accessed to facilitate a higher level of synthesis later in producing your final ePortfolio. For non ePortfolio students, you should structure your journal with meaningful headings, which will help you to develop a summary reflection of the semester as your last journal entry.

• Assignments (Check Schedule for Due dates): 1. Data Science (15%)

Understand the nature of data analytics in context. Understand the skill set of data scientists.

2. Data Preparation: Extract, Transform and Load (ETL) (30%)
Extract the relevant data from original sources (the raw data); transform raw data to appropriate format; load the transformed data to a database.

3. Data Analysis and Modeling (30%)
Explore the transformed data to derive meaningful results (statistical analysis, pattern

recognition, trend visualization)

SP-2014-INSC-592.pdf

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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 18, 2014, in Big Data Analytics, Coursework and tagged . Bookmark the permalink. Leave a comment.

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