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

BDA 101 - Data Analytics and Modelling C01

Academic Credit Value:
3 units
Course Delivery Mode:
Hours of Study:
36 hours
Course Prerequisite(s):
Program Admission
Course Anti-requisite(s):
No Anti-requisite(s)
Instructor Name:
Quang Nguyen-Luong
Course Dates:
01/14/2020 - 03/31/2020

Required Course Materials:
Required readings and materials will be posted on the Avenue to Learn course site.
Optional Course Materials:
Supplemental readings and materials will be posted on the Avenue to Learn course 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 Centre for Continuing Education (CCE) information.

The course focuses on the fundamental principles of big data, data analytics and data modelling. Students will learn how big data analytics has evolved to impact various industries and business sectors. The course will start with an introduction to Big Data then it will present Descriptive and Inferential Statistics, Linear Algebra and Matrix Computations, Linear and Non-Linear Regression, Visual Analytics and Optimization Modelling. 

Learning Outcomes:

Upon completion of this course, students will: 

  • Identify and employ "R" and/or Python programming language. 
  • Recognize basic statistical definitions. 
  • Use linear algebra and matrix computations. 
  • Apply algorithms to solve systems of equations. 
  • Practice optimizations algorithms. 
  • Attribute linear regressions to data. 
  • Attribute nonlinear regression to data. 
  • Implement tree-based methods to datasets. 
  • Visualize data and modelling results. 
Course Evaluation

The final grade is calculated based on the following components: 

Lab Activities/Assignments: 100% (11 labs will be assigned.The top scores from 10 of 11 labs will be used to calculate the final grade)
Course Format:
This course is designed to present the fundamental concepts and theories in data analytics and modelling and promotes the application to the workplace and professional practice. Course activities will include instructor presentations, required readings, and experiential learning activities (i.e. case studies, lab activities, 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. 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):
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
(Please note that CCE will adhere to a zero tolerance application of the policy)
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 result 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, including copying solution sets.
2. Improper collaboration in group work.
3. Copying or using unauthorized aids in tests and examinations.
Academic Accommodations:
Students who require academic accommodation should contact Student Accessibility Services (SAS).
Staff at SAS will evaluate your learning needs and, if required, will provide a letter for the course
instructor. It is the student’s responsibility to inform the instructor and Program Manager of any
accommodation requirements at the start of the course. For more information, contact SAS at ext.
28652 or visit
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.  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.

This course may use 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., on-line 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:

Session #

Lesson/Module Title

Evaluation (best 10 of 11 lab scores will apply to the final grade)


Introduction to the Course What is Data Analytics?



Overview of R

Lab: Introduction to R


Examples of Analytics Problems Modelling for Analytics

Lab: Simple Example in R

Lab: Modelling


Basic Statistics I

Lab: Descriptive Statistics


Basic Statistics II

Lab: Inferential Statistics


Topic: Review of Linear Algebra and Matrix Computations I

Lab: Matrix Operations


Review of Linear Algebra and Matrix Computations II

Lab: Solving Systems of Linear and Non-Linear Equations


Optimization Modeling

Lab: Modeling and Solving Optimization Problems


Introduction to Linear Regression

Lab: Linear Regression


Introduction to Nonlinear Regression and Decision Trees

Lab: Splines and GAMS



Lab: Visual Analytics


Data project cycle: Communicating results and Operationalizing the project