Download INFS 622 Syllabus - Department of Information Sciences and

AIT 580-001 Syllabus – v1 August 3, 2013 Initial Syllabus (DRAFT)
Instructor:
C. Randall Howard, Ph.D., PMP
Graduate Assistant:
Office:
Volgeneau Engineering Building Room 5323
Office:
Phone:
(703) 899-3608
Phone:
E-mail:
[email protected]
E-mail:
Office Hours:
by appointment
Office Hours:
Course #:
Section:
Title:
AIT 580
001
Analytics: Big Data to Information
CRN:
Term:
Time:
Building:
Room:
77766
Fall 2013
Tuesday, 19:20-22:00
Innovation Hall
137
TBD
by appointment
TBD
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 reference is NOT to be distributed, posted or used outside of the
INFS622 session. The material is copyrighted and is Intellectual Property of various parties.
Course Themes:
An Overview of Leadership in Big Data
Course 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.
Learning Objectives:
 Gain appreciation for Big Data Intelligence Landscape and Challenges
 Contribute to shape problem & solution space
 Become familiar with using processing and analytic with tools and techniques
Grading
Table 1. Grading Distribution
Item
Percentage
Individual Assignments
40%
Project / Case Study Work
45%
Professor's Discretion
15%
Table 2. Grading Scale
Letter Grade
Numerical Range
A+
97-100
A
92-96
A90-91
B+
B
BC+
C
C-
88-89
82-87
80-81
78-79
72-77
70-71
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 responsible for 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.
Other assignments:
The professor may assign homework for individuals, groups, or the class as a whole.
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 Submissions
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, with the exception of the project that are due before class begins on presentation day. There will
be a 10% penalty per day for late submissions unless otherwise specified.
All submissions’ file names need to indicate student or group names.
a. For individual submissions, use this format:
LastName_First_Name_AssignmentName
b. For group submissions, questions, etc. for the Professor,
i. CLEARLY mark the subject of the item as w/ ATTN TO PROFESSOR: subject (I do not monitor group
discussion areas)
ii. Send a follow-up email to the Professor that the item has been posted
iii. For Submissions, use this format:
Group#_ArtifactName_State (eg.,Initial, Draft, Final), Version (e.g. #)
iv. Submit on group’s File Exchange area on Blackboard
ALL submissions should be in MS Word, unless otherwise specified. In other words, DO NOT SUBMIT PDF’s – we cannot
effectively provide feedback on .PDF’s.
A 10% penalty may be assessed for not following these instructions!
Electronic Devices
Laptops are allowed for the purpose of taking notes during class only. Phone usage is discouraged, and is only allowed in
the case of personal or business emergencies. If we suspect that these allowances are being abused, then restrictions will
be enforced.
Academic Integrity
The integrity of the University community is affected by the individual choices made by each of us. GMU has an Honor Code
with clear guidelines regarding academic integrity. Three fundamental and rather simple principles to follow at all times are
that: (1) all work submitted be your own; (2) when using the work or ideas of others, including fellow students, give full credit
through accurate citations; and (3) if you are uncertain about the ground rules on a particular assignment, ask for
clarification. No grade is important enough to justify academic misconduct. Plagiarism means using the exact words,
opinions, or factual information from another person without giving the person credit. Writers give credit through accepted
documentation styles, such as parenthetical citation, footnotes, or endnotes. Paraphrased material must also be cited, using
MLA or APA format. A simple listing of books or articles is not sufficient. Plagiarism is the equivalent of intellectual robbery
and cannot be tolerated in the academic setting. If you have any doubts about what constitutes plagiarism, please see me.
As in many classes, a number of projects in this class are designed to be completed within your study group. With
collaborative work, names of all the participants should appear on the work. Collaborative projects may be divided up so
that individual group members complete portions of the whole, provided that group members take sufficient steps to ensure
that the pieces conceptually fit together in the end product. Other projects are designed to be undertaken independently. In
the latter case, you may discuss your ideas with others and conference with peers on drafts of the work; however, it is not
appropriate to give your paper to someone else to revise. You are responsible for making certain that there is no question
that the work you hand in is your own. If only your name appears on an assignment, your professor has the right to expect
that you have done the work yourself, fully and independently.
GMU is an Honor Code university; please see the Office for Academic Integrity for a full description of the code and the
honor committee process. The principle of academic integrity is taken very seriously and violations are treated gravely.
What does academic integrity mean in this course? Essentially this: when you are responsible for a task, you will perform
that task. When you rely on someone else’s work in an aspect of the performance of that task, you will give full credit in the
proper, accepted form. Another aspect of academic integrity is the free play of ideas. Vigorous discussion and debate are
encouraged in this course, with the firm expectation that all aspects of the class will be conducted with civility and respect
for differing ideas, perspectives, and traditions. When in doubt (of any kind) please ask for guidance and clarification.
It is your responsibility to know and to follow Mason’s policy on academic integrity (http://oai.gmu.edu/honor-code/masonshonor-code/).
The professor utilizes the tools such as SafeAssign (provided as part of Blackboard) to check assignments against
published resources AND other students’ work.
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.
Disability Accommodations
If you have a documented learning disability or other condition that may affect academic performance you should: 1) make
sure this documentation is on file with Office of Disability Services (SUB I, Rm. 4205; 993-2474;http://ods.gmu.edu) to
determine the accommodations you need; and 2) talk with me to discuss your accommodation needs.
If you are a student with a disability and you need academic accommodations, please see me and contact the Office of
Disability Services (ODS) at 993-2474, http://ods.gmu.edu. All academic accommodations must be arranged through the
ODS.
If you have a learning or physical difference that may affect your academic work, you will need to furnish appropriate
documentation to the Office of Disability Services. If you qualify for accommodation, the ODS staff will give you a form
detailing appropriate accommodations for your instructor. In addition to providing your professors with the appropriate form,
please take the initiative to discuss accommodation with them at the beginning of the semester and as needed during the
term. Because of the range of learning differences, faculty members need to learn from you the most effective ways to
assist you. If you have contacted the Office of Disability Services and are waiting to hear from a counselor, please tell me.
Mason Diversity Statement
George Mason University promotes a living and learning environment for outstanding growth and productivity among its
students, faculty and staff. Through its curriculum, programs, policies, procedures, services and resources, Mason strives to
maintain a quality environment for work, study and personal growth.
An emphasis upon diversity and inclusion throughout the campus community is essential to achieve these goals. Diversity is
broadly defined to include such characteristics as, but not limited to, race, ethnicity, gender, religion, age, disability, and
sexual orientation. Diversity also entails different viewpoints, philosophies, and perspectives. Attention to these aspects of
diversity will help promote a culture of inclusion and belonging, and an environment where diverse opinions, backgrounds
and practices have the opportunity to be voiced, heard and respected.
The reflection of Mason’s commitment to diversity and inclusion goes beyond policies and procedures to focus on behavior
at the individual, group and organizational level. The implementation of this commitment to diversity and inclusion is found in
all settings, including individual work units and groups, student organizations and groups, and classroom settings; it is also
found with the delivery of services and activities, including, but not limited to, curriculum, teaching, events, advising,
research, service, and community outreach.
Acknowledging that the attainment of diversity and inclusion are dynamic and continuous processes, and that the larger
societal setting has an evolving socio-cultural understanding of diversity and inclusion, Mason seeks to continuously
improve its environment. To this end, the University promotes continuous monitoring and self-assessment regarding
diversity. The aim is to incorporate diversity and inclusion within the philosophies and actions of the individual, group and
organization, and to make improvements as needed.
Privacy
Students must use their MasonLive email account to receive important University information, including messages related to
this class. Seehttp://masonlive.gmu.edu for more information.
If you have special circumstances arise that may impede your performance in the class, please let me know. Your situation
will be held in the strictest of confidence. It may require informing my TA or administration as needed so that they can also
support you as well. Mason offers a great deal of help in many areas, but we cannot help unless we know.
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
 Schedule WILL change as needed to facilitate learning according to personality & makeup of the class
#
Date
1
8/ 27
2
Session Topics
Speakers
Course Overview
Howard
9/3
Metadata vs. Meta-Tagging vs. Data
Howard
3
9/10
Solving Big Data Problems with Applied Statistics
4
9/17
Big Data Intelligence Landscape
5
9/24
The Big 3: Intel, Health, Financial
6
7
--
10/ 1
10/ 8
10/15
8
10/22
9
10
11
12
13
10/29
11/5
11/12
11/19
11/26
Big, Notional Problem Solving
Big Data Cloud Processing
No Class
Organizational Values and Decision-Making
Enterprise Architecture Principles & Techniques
Evaluation Criteria
RDBMS’s Journey into Big Data
Mastering the Bureaucracy
Group Project Reviews
Securing Data and Privacy
Data Quality
Forbes
Aiken
Mattox
McCormick
Parramore
Quinn
Sagan
Curry
14
12/3
Case Study Report Day
15
12/10
Final Course Reports
Howard
Foxwell
Magee
Groups
Raines
Howard
Team
Howard
Class
Reference
Lecture: Howard - Overview v1-2.pptx
Read-aheads:
 Big Data in the US Intel Community 19Feb12 Version.pdf
 What_is_Data_Science.pdf
 emc-data-science-study-wp.pdf
 References in AIT690-001 Overview.pptx
Howard - So-What, Data vs. Metadata, Big Data Sufficiency
Forbes - Statistics_as_Metadata.pptx
Mattox - Big Data and Massive Analytics3-1.pptx
McCormick Pedigree & Lineage in Information Sharing.pptx
Quinn - Leading Sustainable Change.pptx
Sagan - We don't know what we're talking about.pptx
Curry-Cloud Lecture (March 3 2013).pptx
Howard - Wicked Problems, Learning Organization & Decision
Making.pptx
TBS
Magee - Big Data Bureaucracy v2.ppt
TBS
TBS
Howard - Big Data Quality.pptx
TBS
TBS