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 205 - Statistical Analysis for Data Science

Academic Credit Value:
3 units
Course Delivery Mode:
Hours of Study:
30 hours
Course Prerequisite(s):
Grade 12 U level Mathematics (Advanced Function, or Calculus and Vectors, or Mathematics for Data Management, or Mathematics for College Technology); University or college introductory course in Statistics; Statistics for Data Analytics
Course Anti-requisite(s):
Instructor Name:
Course Dates:
01/26/2020 - 03/29/2020

Required Course Materials:
Materials will be posted to the supplemental site in Avenue to Learn
Optional Course Materials:
Other readings and resources will be posted in the Avenue to Learn course shell
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 below for further course and Centre for Continuing Education (CCE) information. 

This course provides a foundation for exploring data through computing and statistical analysis. The focus is placed on the structure and applications of probability, statistics, computer simulation and data analysis for students exploring the field of data science. This course builds upon introductory statistics courses and is designed for students with experience/study in programming, calculus, and algebra. Programming in R will be used throughout the course.

Learning Outcomes:

Upon completion of the course, students will be able to:

1.  Recognize basic concepts of probability & statistical definitions.
2.  Exploit visualization techniques to determine/verify a correlation between multiple quantities.
3.  Use linear algebra tools to calculate descriptive statistical quantities for multivariate statistical systems.
4.  Apply statistical sampling techniques to identify probability distributions.
5.  Employ the linear regression method to characterize the relationship between dependent variable and independent variables.
6.  Apply classification and clustering strategies for data structure analysis.
7.  Use a statistical software package to perform data analysis for qualitative and quantitative problem-solving.

Course Evaluation

The final grade is calculated based on the following components:

  • In-lab Tutorials: 20.0% (4@5.0% each)
  • In-lab Quiz: 20.0% (4@5.0% each)
  • In-lab Assignments: 30.0% (2@15.0% each)
  • Course Project: 30.0%


Course Format:

This course is designed to present the fundamental concepts and theories in statistics and data analysis 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.  All coursework must be submitted by the last official 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

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, and external websites.  Students should be aware that, when they access the electronic components of this course, private information such as first and last names, usernames 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 where appropriate. 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:

Week #

Lesson/Module Title



Basic probability & Random variables

In-Lab Tutorial



Descriptive Statistics: location & Variability

Quiz 1 and Lab Tutorial 1



Basic visualization techniques

Quiz 2 and Lab Tutorial 2



Exploring Distributions (Discrete & Continuous)

In-lab Assignment 1



Data & Sampling Distributions

Quiz 3 and Lab Tutorial 3

/Project Proposal submission


Linear Algebra & Matrix operations

Quiz 4 and Lab Tutorial 4-1

/Project Review session


Multivariate systems

In-Lab Tutorial 4-2

/Project Presentation


Linear Regression

In-lab Assignment 2



Introduction to classification & Clustering

Optional Lab Tutorial / Project Completion and submissions