Lifelong Learning for a Brighter World

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Foundations of Analytics

Business Intelligence, Data Analysis and Data Science

Your Introduction to the World of Analytics

OSI 101 - Tradecraft and Techniques C01

Academic Credit Value:
None
Course Delivery Mode:
Virtual Classroom
Hours of Study:
30 hours
Course Prerequisite(s):
Basic skills with MS Excel, Word Familiarity with Internet search, browsers and social media
Course Anti-requisite(s):
n/a
Instructor Name:
Sami Khoury
Course Dates:
05/04/2020 - 06/29/2020



Required Course Materials:
Participants are to bring a laptop to each class. Resources/materials will be provided during class.
Optional Course Materials:
Course Description:

Participants are advised to retain course outlines for future use in support of applications for employment.
Refer to the Policy & Procedure section for further course and Continuing Education (CE) information
.

This course covers the techniques, tools and methods of conducting effective Open Source Intelligence (OSINT) research and tracking to gain insights about people, organizations, and issues for business or investigative purposes. Participants will learn how to accurately assess and efficiently monitor new sources of publicly available information, including the Internet and social media, collect and process relevant data, and characterize and analyze information, while still maintaining their online security and anonymity. 

The course will also cover principles of data literacy, data visualization, and data management, and will teach participants the skills required to begin to harness the awesome power of publicly available information to gain and maintain a competitive edge in most modern industries.
Learning Outcomes:

By the end of this course, participants will be able to:  

  • Apply OSINT techniques and tools to find and accurately assess new sources of public information.
  • Ascertain the origins, accuracy, credibility, and usefulness of public content.
  • Maintain security and anonymity while conducting OSINT investigations.
  • Effectively collect and aggregate information on issues of interest from a wide range of sources, and    efficiently monitor those sources in real time.
  • Compile informative OSINT profiles and reports. 
Course Evaluation

Participants are assigned a Pass/Fail notation based on the following course activities:

  • In-class exercises
  • Capstone exercise
Course Format:
This course is designed to present the fundamental concepts and theories in open source intelligence and promote the application to the workplace and professional practice. Course activities will include instructor presentations and experiential learning activities. 
Assignment Submission:
Course assignments are submitted in class on the specified due date
Late Coursework:
All coursework must be submitted by the last scheduled date of the class.  Requests for extensions must be submitted to the Instructor before the assignment due date.

Policy & Procedures:

Academic Regulations (Attendance, Coursework, Tests/Exams):
In accordance with McMaster University’s General Academic Regulations, “it is imperative that students
make every effort to meet the originally scheduled course requirements and it is a student’s
responsibility to write examinations as scheduled.” Therefore, all students are expected to attend and
complete the specific course requirements (i.e. attendance, assignments, and tests/exams) listed in the
course outline on or by the date specified. Students who need to arrange for coursework
accommodation, as a result of medical, personal or family reasons, must contact the course instructor
within 48 hours of the originally scheduled due date. It is the student’s responsibility to contact the
Program Manager/Program Associate to discuss accommodations and procedures related to deferred
tests and/or examinations within 48 hours of the originally scheduled test/exam, as per policy. Failure
to contact the course instructor, in the case of missed coursework, or the Program Manager/Program
Associate, in the case of a missed test/examination, within the specified 48 hour window will result in a
grade of zero (0) on the coursework/exam and no further consideration will be granted.

*Note: Supporting documentation will be required but will not ensure approval of accommodation(s).
Academic Integrity
 You are expected to exhibit honesty and use ethical behaviour in all aspects of the learning process. Academic credentials you earn are rooted in principles of honesty and academic integrity. Academic dishonesty is to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage. This behaviour can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: “Grade of F assigned for academic dishonesty”), and/or suspension or expulsion from the university.

 It is your responsibility to understand what constitutes academic dishonesty. For information on the various types of academic dishonesty please refer to the Academic Integrity Policy, located at http://www.mcmaster.ca/academicintegrity/

 The following illustrates only three forms of academic dishonesty:

  1. Plagiarism, e.g. the submission of work that is not one’s own or for which other credit has been obtained.
  2. Improper collaboration in-group work.
  3. Copying or using unauthorized aids in tests and examinations.
Academic Accommodations:
 

Participants with disabilities who require academic accommodations must contact the Student Accessibility Centre (SAS) to meet with an appropriate Disability Services Coordinator. To contact SAS, phone 905-525-9140 ext. 28652, or email sas@mcmaster.ca. For further information, consult McMaster University’s Policy for Academic Accommodation for Students with Disabilities.

On-line Elements:

In this course, we will be using on-line elements, which may include email, Avenue to Learn, WebEX, and external web sites.  Participants should be aware that, when they access the electronic components of this course, private information such as first and last names, user names for the McMaster e-mail accounts, and program affiliation may become apparent to all other participants in the same course. The available information is dependent on the technology used. Continuation in this course will be deemed consent to this disclosure. If you have any questions or concerns about such disclosure please discuss this with the instructor.

Turnitin.com:
Course Changes:
The instructor reserves the right to modify elements of the course and will notify participants accordingly.
Course Withdrawal Policy:

Policies related to dropping a course and course withdrawals are posted to Continuing Education’s program webpage, FAQs & Policies (https://www.mcmastercce.ca/cce-policies#Dropping).

Storm Closure Policy:
 In the event of inclement weather, the Centre for Continuing Education will abide by the University’s Storm Closure Policy: https://www.mcmaster.ca/policy/Employee/storm_emergency_policy.pdf, and will only close if the University is closed. All in-class courses, exams and room bookings by internal and external clients will be cancelled if the Centre for Continuing Education is closed. On-line courses will take place as scheduled.
Grading Scale:
Course Schedule:
 

Session

Topic & Materials

1

Open Source Intelligence and Security

· Introduction

· Data, information and intelligence

· Open Source Intelligence (OSINT)

· Opportunities and challenges

· Safeguarding security and privacy

 

2

Data and Sources

· Security and privacy (continued)

· Data and media landscapes

· Source assessment and profiling

· Information verification

 

3

Research and Content

 

· Verification (continued)

· Search techniques and tools

· Content and context

 

4

OSINT Tools

· Basic tools

· Web/developer tools

· Understanding source code

· Advanced/hacker tools

 

5

OSINT Workflows

· Investigation workflows

· Knowledge mapping and gap analysis

· Lead generation and resolution

 

6

Monitoring and Collection

· Source monitoring process and tools

· Automated monitoring

· Selection criteria

· Data collection process and tools

· Structuring data and the role of metadata

 

7

Processing and Production

· Data management and retrieval

· Information aggregation and processing

· Compiling intelligence reports (introduction)

 

8

Data Analysis Techniques

· Network analysis

· Language processing and topic modelling

· Sentiment analysis

· Compiling intelligence reports (expanded)