AIT 690-001 Syllabus V1january1, 2013Initial Syllabus (DRAFT)

AIT 690-001 Syllabus– v1January1, 2013Initial Syllabus (DRAFT)

Course #:AIT 690-001

Section:001

CRN:20863

Catalog Title:AIT 690 - Adv Topics Applied Technology

Course Title:Leadership in Big Data Intelligence with Small Details and Time

Term:Spring2013

Time: Tuesday, 19:20-22:00

Building:Planetary Hall

Room: 126

Pre-Requisites: Admission to Mason’s Applied IT program, 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 who have allowed Mason to use it for the Big Data Intelligence topic.

Course Themes:Leadership in Big Data Intelligence with Small Details and Time

Exploring Metadata in Big Data Intelligence

Course Description:

Explore leadership, management, technical and analytical issues, solutions and associated gaps 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. insurance, financial, national security, etc.) where frequently there is only a limitedwindow of time (SmallTime) to avert an undesirable event or seize the opportunity.

OR:“What are we learning”, “What do we know so far” and “We don’t know what we are doing” about Big Data Intelligence (BDI).

Learning Objectives:

• Gain appreciation for Big Data Intelligence Landscape and Challenges
• Understand metadata's 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 techniques

Grading

Table 1.Grading Distribution

Table 2. Grading Scale

Individual Assignments:

The individual assignment focus on the problem-solving 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 report MUST be each students’ individual and independent work.

Group Project &/or Case Study Reports:

There will be a group exploration project. Each team is responsiblefor examining key industries or domains that are facing big data challenges, such as major brick-and-mortar retail (e.g. Walmart), web-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 operational 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 technically-capable person to help support the team with the course lab. Each team member's individual contribution to the final documents must be clearly identified. Each group will be called on to present material throughout the semester.

Professor’s Discretion:

Participation is a portion of both the group project 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 Sumissions

All 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.
2. For individual submissions, use this format:

LastName_First_Name_AssignmentName

1. For group submissions, questions, etc. for the Professor,
2. CLEARLY mark the subject of the item as w/ ATTN TO PROFESSOR: subject (I do not monitor group discussion areas)
3. Send a follow-up email to the Professor that the item has been posted
4. For Submissions, use this format:

Group#_ArtifactName_State (eg.,Initial, Draft, Final), Version (e.g. #)

1. Submit on group’s File Exchange area on Blackboard

1. ALL submissions should be in MS Word, unless otherwise specified. In other words, DO NOT submit .PDF’s – I cannot provide feedback easily w/ .PDF’s.
2. A 10% penalty may be assessed for not following these instructions!

MORE IMPORTANT NOTES:

Academic Integrity. It is your responsibility to know and to follow Mason’s policy on academic integrity (

SafeAssign. The professor utilizes the tool provided as part of Blackboard to check assignments against published resources AND other students’ work.

Honor Code Statement:

As with all GMU courses, INFS 622 is governed by the GMU Honor Code. In this course, all assignments, exams, and project submissions carry with them an implicit statement that it is the sole work of the author, unless joint work is explicitly authorized. Help may be obtained from the instructor or other students to understand the description of the problem and any technology, but the solution, particularly the design portion, must be the student's own work. If joint work is authorized, all contributing students must be listed on the submission. Any deviation from this is considered an Honor Code violation. (© Jeff Offutt).

To stay safe:

• Provide citations for your work – group and individual – even if it is “adapted from”.
• Do not work in groups to complete individual work.
• Do not copy and paste material from the text except for short, pithy definitions that cannot necessarily be re-worded easily.

ODS Statement. If you have a disability and wish academic accommodations, please see the Professor and contact the Office of Disability Services (703) 993-2474, (

AIT690-001 Class Schedule

V0.01: Session 0 Adjustment

Schedule Notes:

• Order is (re-)arranged to facilitate more time to apply the discussion to the project artifacts
• Project Artifacts w/in Lectures are highlighted in yellow.
• Schedule WILL change as needed to facilitate learning according to personality & makeup of the class
• Items marked w/ a “[D] party:” indicate a deliverable from the party: listed (e.g., Students, Groups, Professor)
• Color Legend:

• Introductions
• Roster & Profiles
• Genesis of Course
• Course Overview
• Students’ Objectives?

• The Tools & “Lab Supplies”

• Lecture Slides:AIT690-001 Overview.pptx
• Read-Aheads:
• Big Data in the US Intel Community 26Dec Large-1
• What_is_Data_Science.pdf
• emc-data-science-study-wp.pdf
• References in AIT690-001 Overview.pptx
• Reference:
• Lecture_Series_I_Material/Gus Hunt GMU_Big_Data_Course-final.ppt
• Big Data Intelligence Systems Leadership & Operation Executive Lecture Series I.pdf

• Big Data “So-What”
• What is Metadata? "Meta-tagging"?
• What is the difference between Meta-tagging, Metadata and data
• How can metadata help? (Big-Data Metadata)
• Sufficiency Principle

• AIT690-001 So-What, Data vs. Metadata, Big Data Sufficiency.pptx
• Read-Aheads:
• Finkelstein&Aiken for AIT690-001.pdf
• Big Data Principles of Sufficiency v1-1.pdf
• Reference:
• TBS

• Major types of statistics
• Major types of problems
• Applying data science & statistics to solve the problems
• Incremental Problem Solving (Hypothesis, Implement, Revise)
• Lab Assignment

• [D] Lab Work Assigned
• Lecture Slides: To be supplied

• Read-Aheads:
• To be supplied

• Reference:
• To be supplied

• Data Basics: What makes data so gnarly?

• What makes Big Data so challenging?

• Aiken
• Mattox

• Lecture Slides:
• DMP Appraisal Instructions.key (Aiken)
• Big Data and Massive Analytics Short Class (Maddox)
• Read-Aheads:
• Practicing Data Management - Chapter 6.pdf
• Reference:
• “Mythical Man Month” Posted
• “Sliver Bullet”: To be supplied
• DM Problems:

• Political Architectures
• How is metadata vital in Information Sharing?
• Why is Pedigree & Lineage so important?
• How can metadata (e.g. P&L or traceability) in transitioning from strategy to tactical operations have an important and vital role?

• Lecture Slides: To be supplied
• Read-Aheads:
• DNI Material on Info Sharing
• P&L Slides (To be supplied)
• References:
• To be supplied

• Thinking outside of the box
• We don't know what we are doing?
• Leveraging Conflict & "Masterminding"

• Lecture Slides: To be supplied
• Read-Aheads:
• Conflict material?
• Sagan's Material
• Reference
• To be supplied

• Thinking outside of the box
• We don't know what we are doing?
• Leveraging Conflict & "Masterminding"

• Lecture Slides: To be supplied
• Read-Aheads:
• To be supplied

• Reference:
• To be supplied

• Organizational Values and Decision-Making
• Enterprise Architecture Principles & Techniques
• Evaluation Criteria

• Why is Big Data Intelligence a "Wicked Problem"?
• How can business basics in Engineering & Operations determine the important metadata
• What is Enterprise Architecture? How do the principles apply?
• How can we harness uncertainty by dealing w/ the predictable aspects?
• How can metadata facilitate Risk Mitigation?

• Lecture Slides:
• AIT690-001 Wicked Problems, Learning Organization & Decision Making.pptx
• Read-Aheads:
• TEN#38 Enterprise Architecture as Strategy.pdf

• Reference:
• FSAM_Complete_v1_1.pdf(
• ValueMeasuring_Methodology_HowToGuide_Oct_2002.pdf(
• introduction-to-vmm-bah-0ct-2004.pdf((

• Understanding purpose and power of bureaucracy
• How bureaucracy effects choosing which metadata should be used?
• How metadata can facilitate multiple disciplines?

• Lecture Slides: Big Data Bureaucracy
• Read-Aheads:
• To be supplied

• Reference:
• To be supplied

• Why are Politics & Laws, Contracts & Ethics so vital?
• What effect do politics, laws, contracts, ethics play on which metadata should be used?

• Leadership & Change in Culture
• How can we transition culture to accept:
• Change is imperative
• Learning gap is not trivial
• Business as normal will not suffice

• Latiff
• Quinn

• Lecture Slides: To be supplied
• Read-Aheads:
• Harvard Business Studies: 606003-VW of America-Managing IT Priorities (
• Reference:
• Strategic Planning Research Cutouts.docx

• Data Encryption
• Key Management

• Lecture Slides: To be supplied
• Read-Aheads:
• To be supplied

• Reference:
• To be supplied

• Grasping Data Fitness
• Measuring Quality
• Performance & Throughput
• Course Wrap-up
• Course Evaluations

• Lecture Slides: AIT690-001 Data Quality.pptx
• Read-Aheads:
• Talburt’s Slides References
• Get w/ Talburt

• Reference:
• Data quality:

• Case Study reports

• [D] Teams: Case Study Reports & Presentations
• Lecture Slides: AIT690-001 Wrapup.pptx

• Read-Aheads:
• To be supplied

• Reference:
• To be supplied