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1、AIT 690-001 Syllabus v1 January 1, 2013 Initial Syllabus (DRAFT)Instructor: Office: Phone: E-mail: Office Hours:C. Randall Howard, Ph.D., PMPVolgeneau Engineering Building Room 5323(703) 899-3608chowardgmu.eduby appointmentGraduate Assistant: Office: Phone: E-mail: Office Hours: TBDby appointmentTBD
2、Course #:AIT 690-001Section: 001CRN:20863Catalog Title:AIT 690 - Adv Topics Applied TechnologyCourse Title:Leadership in Big Data Intelligence with Small Details and TimeTerm: Spring2013Time: Tuesday, 19:20-22:00 Building:Planetary HallRoom: 126Pre-Requisites: Admission to Masons Applied IT program,
3、 or permission of instructor.Course Readings: Designated w/ session topics below IMPORTANT NOTE: The material posted for reading and is NOT to be distributed, posted or used outside of the AIT690-001 session. The material is copyrighted and is Intellectual Property of the individuals or companies wh
4、o have allowed Mason to use it for the Big Data Intelligence topic. Course Themes:Leadership in Big Data Intelligence with Small Details and TimeExploring Metadata in Big Data IntelligenceCourse Description:Explore leadership, management, technical and analytical issues, solutions and associated gap
5、s in processing an ever-increasing volume of data (Big Data) by leveraging meta-tags and metadata (Small Details). The end-goal is to increase the throughput of finding credible “facts of interest” (Intelligence) that represent threats to, or even opportunities for, a given industry or domain (e.g.
6、insurance, financial, national security, etc.) where frequently there is only a limited window of time (Small Time) to avert an undesirable event or seize the opportunity. OR:“What are we learning”, “What do we know so far” and “We dont know what we are doing” about Big Data Intelligence (BDI).Learn
7、ing Objectives: Gain appreciation for Big Data Intelligence Landscape and Challenges Understand metadatas role and gain insights in Big Data Intelligence Systems (BDIS) Contribute to shape problem & solution space Become familiar with using processing and analytic with tools and techniquesGradingTab
8、le 1. Grading DistributionItemPercentageIndividual Assignments45%Project / Case Study Work40%Professors Discretion15%Table 2. Grading ScaleLetter GradeNumerical RangeA+97-100A92-96A-90-91B+88-89B82-87B-80-81C+78-79C72-77C-70-71Individual Assignments:The individual assignment focus on the problem-sol
9、ving aspects related to the processing and analytics within BDIS. The assignment entails using tools and developing a report with observations, assessments, lessons learned, etc. Each student is allowed to gain assistance from other students or outside assistance on the “tool” aspect; however, the r
10、eport MUST be each students individual and independent work. Group Project &/or Case Study Reports:There will be a group exploration project. Each team is responsible for examining key industries or domains that are facing big data challenges, such as major brick-and-mortar retail (e.g. Walmart), we
11、b-based companies(e.g. Facebook, Groupon), banking, insurance, national security, etc. The teams should examine, analyze and report on both the risks and opportunities as separate aspects. The major facets of bureaucracy, technology and analytics should be included in the assessment. Strategic and o
12、perational considerations should also be considered. Alternatives, tradeoffs and recommendations need to be reported. Each group will select a team coordinator or leader who will help coordinate the overall progress of the team. Additionally, the group makeup will need to have at least one technical
13、ly-capable person to help support the team with the course lab. Each team members individual contribution to the final documents must be clearly identified. Each group will be called on to present material throughout the semester. Professors Discretion:Participation is a portion of both the group pr
14、oject and individual grades. This has been a particular challenge that we will be addressing throughout the semester in various, ad-hoc manners depending on how proactive the class is in averting “ad-hoc manners”. Warning: “ad-hoc” manners are not necessarily the preferable option either.All Sumissi
15、onsAll work must be submitted at the scheduled time and place unless prior arrangements are made. Missed reports cannot be made up without these prior arrangements.All assignments will be graded on correctness as well as style and presentation. Each assignment is due on the announced date before 12 midnight. There will be a strictly enforced 10% penalty per day for late submissions unless otherwise specified. IMPORTANT NOTES: 1. All submissions file names need to indicate student or group names.a. For individual s
