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Foundations of Analytics

Business Intelligence, Data Analysis and Data Science

Your Introduction to the World of Analytics

DAT 103 - Business Intelligence & Data Analytics C01

Academic Credit Value:
3 units
Course Delivery Mode:
Virtual Classroom
Hours of Study:
24 hours
Course Prerequisite(s):
Course Anti-requisite(s):
Instructor Name:
James Green
Course Dates:
06/11/2020 - 07/30/2020

Required Course Materials:
Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th Edition, Pearson Publishing
Optional Course Materials:
Supplemental and additional resources will be posted to the Avenue to Learn site
Course Description:

Students are advised to retain course outlines for future use in support of applications for employment or transfer of credits.
Refer to the Policy & Procedure section for further course and ​McMaster Continuing Education information

Learn to apply data analytics skills to the area of business intelligence (BI). Focus is placed on the components of business intelligence project lifecycle such as project planning, BI tool selection, data modelling, ETL design, BI application design and deployment and reporting. This course is designed for individuals interested in BI practices and analysis without a detailed focus on statistical analysis and computer programming.

Learning Outcomes:

By the end of this course, the participants will be able to make recommendations for solutions to case studies drawn from real-world experiences using Business Intelligence (BI) methodologies, processes, architectures and technologies. 

Specifically, students will:

Examine the business intelligence project lifecycle
Describe business intelligence methodology and concepts
Understand various types of analytics and review selected applications
Explore the nature of data as it relates to business intelligence and analytics
Learn different types of data visualization
Explain data integration and the extraction, transformation and load (ETL) process
Review standardized data mining processes

Course Evaluation

BI lab activities: 100% (A series of weekly activities will be assigned; each weighted equally)

Note: The course evaluation breakdown may change since the date this outline is posted online. 

Course Format:
This course is designed to present the fundamental concepts and theories in business intelligence and promote the application to the workplace and professional practice. Course activities will include instructor presentations, required readings and experiential learning activities (i.e. case studies, group discussions, projects, etc.).
Assignment Submission:
Course assignments are submitted to the appropriate A2L Assignment folder by the specified due date
Late Coursework:
Late submissions will be penalized 2% per day (including weekends and holidays) up to seven (7) days past the due date. After seven days, the Assignment folder will close, no further assignments will be accepted, and a grade of zero(0) will be assigned for the course work item unless otherwise specified by the Instructor.  No submissions will be accepted past the official end date of the course.  Requests for extensions must be submitted to the Instructor before the assignment due date (see Coursework Policies).  Extensions are permitted for exceptional circumstances only; supporting documentation may be requested.   

Policy & Procedures:

Academic Regulations (Attendance, Coursework, Tests/Exams):
Academic Regulations (Attendance, Coursework, Tests/Exams):
In accordance to 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 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, 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

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:
Students 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 For further information, consult McMaster University’s Policy for Academic Accommodationfor Students with Disabilities.
On-line Elements:
In this course, we will be using online elements, which may include email, Avenue to Learn, WebEx, and external web sites.  Students 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 students 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 course instructor.
In this course, we may be using a web-based service ( to reveal plagiarism. Students will be expected to submit their work electronically to and in hard copy so that it can be checked for academic dishonesty. Students who do not wish to submit their work to must still submit a copy to the instructor. No penalty will be assigned to a student who does not submit work to All submitted work is subject to normal verification that standards of academic integrity have been upheld (e.g., online search, etc.). To see the Policy, please go to McMaster Academic Integrity Policy.
Course Changes:
The instructor reserves the right to modify elements of the course and will notify students accordingly.
Course Withdrawal Policy:
Policies related to dropping a course and course withdrawals are posted to the Centre for Continuing Education’s program webpage, FAQs & Policies (
Storm Closure Policy:
 In the event of inclement weather, the Centre for Continuing Education will abide by the University’s Storm Closure Policy:, 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:


Equivalent Grade Point

Equivalent Percentages








































Course Schedule:




Topic & Readings

Assignments/Graded Components


· Course introduction

· Overview of Business Intelligence and Data Analytics

· Introduction to Business Intelligence LifeCycle

Text: Chapter One: An Overview of Business Intelligence, Analytics and Data Science

· Lab Activity #1


· Data in a Business Environment

· Introduction to Statistical Modeling, Descriptive and Inferential Statistics

· Approaches to Data Visualization

· Exploring BI projects

Text: Chapter Two: Descriptive Analytics I: Nature of Data, Statistical Modeling and Visualization

· Lab Activity #2


· Data Warehousing

· The ETL Process

· Selection of a BI project

Text: Chapter Three: Descriptive Analytics II: Business Intelligence and Data Warehousing

· Lab Activity #3



· Data Mining

· Data Mining Processes

· Data Mining Tools

· Privacy Considerations

Text: Chapter Four: Predictive Analytics I: Data Mining Process, Methods and Algorithms

· Lab Activity #4



· Introduction to Text Analytics and Text Mining

· Introduction to Sentiment Analysis

Text: Chapter Five: Predictive Analytics II: Text, Web and Social Media Analysis

· Lab Activity #5


· Foundations of Prescriptive Analytics

· Analytical Decision Modeling

Text: Chapter Six: Prescriptive Analytics II: Optimization and Simulation

· Lab Activity #6


· Data Visualization: Tools and Technologies

· Dashboards

· Bringing the Project Together

· Lab Activity #7


· Future Trends for Business Intelligence

Text: Chapter 8: Future Trends, Privacy and Managerial Considerations in Analytics

Supplemental: Text Chapter 7: Big Data Concepts and Tools

· Lab Activity #8