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For approval of new courses and deletions or modifications to an existing course. Course Approval Form registrar.gmu.edu/facultystaff/curriculum Action Requested: Course Level: x Create new course Inactivate existing course Modify existing course (check all that apply) Title Prereq/coreq Other: College/School: Submitted by: Subject Code: Credits Schedule Type Undergraduate x Graduate Repeat Status Restrictions Grade Type Department: Ext: 3-2799 VSE Robert T. Quinn Number: AIT Effective Term: 580 (Do not list multiple codes or numbers. Each course proposal must have a separate form.) Title: Applied Information Technology Email: [email protected] x Fall Spring Summer Year 2013 Current Big Data & Advanced Analytics Banner (30 characters max including spaces) New Credits: x (check one) Grade Mode: Fixed Variable x (check one) 3 or to Repeat Status: x Not Repeatable (NR) Repeatable within degree (RD) Repeatable within term (RT) (check one) Regular (A, B, C, etc.) Satisfactory/No Credit Special (A, B C, etc. +IP) Prerequisite(s): Graduate Standing Schedule Type: (check one) LEC can include LAB or RCT x Maximum credits allowed: Lecture (LEC) Lab (LAB) Recitation (RCT) Internship (INT) Corequisite(s): Independent Study (IND) Seminar (SEM) Studio (STU) Instructional Mode: x 100% face-to-face Hybrid: ≤ 50% electronically delivered 100% electronically delivered Restrictions Enforced by System: Major, College, Degree, Program, etc. Include Code. Are there equivalent course(s)? Yes x No If yes, please list Catalog Copy for NEW Courses Only (Consult University Catalog for models) Description (No more than 60 words, use verb phrases and present tense) Notes (List additional information for the course) Course provides an overview of Big Data and its use in commercial, scientific, governmental and other applications. Topics include technical and non-technical disciplines required to collect, process and use enormous amounts of data available from numerous sources. Lectures cover system acquisition, law and policy, and ethical issues. It includes brief discussions of technologies involved in collecting, mining, analyzing and using results. Indicate number of contact hours: Hours of Lecture or Seminar per week: 3 Hours of Lab or Studio: When Offered: (check all that apply) x Spring x Fall Summer Approval Signatures 11/26/2012 Date Robert T. Quinn Department Approval 11/26/2012 Date College/School Approval If this course includes subject matter currently dealt with by any other units, the originating department must circulate this proposal for review by those units and obtain the necessary signatures prior to submission. Failure to do so will delay action on this proposal. Unit Name Unit Approval Name Unit Approver’s Signature Date For Graduate Courses Only Graduate Council Member Provost Office Graduate Council Approval Date For Registrar Office’s Use Only: Banner_____________________________Catalog________________________________ revised 11/8/11 Syllabus AIT 580 Big Data & Advanced Analytics Description: Course provides an overview of Big Data and its use in commercial, scientific, governmental and other applications. Topics include technical and non-technical disciplines required to collect, process and use enormous amounts of data available from numerous sources. Lectures cover system acquisition, law and policy, and ethical issues. It includes brief discussions of technologies involved in collecting, mining, analyzing and using results. Prerequisite: Graduate standing Texts: D.J. Patil, Building Data Science Teams, O’Reilly Media, ISBN: 978-1-449-31623-5. http://isrc.gmu.edu/ExecLectureSeries/ExecLectureSeries.html “Big Data in the U.S. Intelligence Community”, a compendium of the impact Big Data and Massive Analytics creates on every facet of an organization. Harvard Business Review, September 2012 Instructors: Charles Randy Howard, Ph.D. Department of Applied Information Technology [email protected], (703) 899-3608 Grading: Team Project: 40% Lab Project: 40% Class Participation: 10% Peer Evaluation: 10% Topics 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. Introduction , Definitions, Terms and Uses Law, Policy, Regulation of Data and Information Technology Development and System Acquisition Big Data Collection and Transmission, Cloud Computing Data Types Data Quality and Data Management Metadata Data Mining Analytic Techniques and Pathologies Data Visualization Ethical Challenges and Provocations in Big Data Case Studies Future trends in Big Data