Post-Baccalaureate Diploma in Marketing and Data Analytics

The Post-Baccalaureate Diploma in Marketing and Data Analytics is designed for students with a bachelor’s degree in business or science who have an advanced proficiency in mathematics. Please see the “data science diagnostic test” button below for an example of the level of knowledge applicants are expected to have. Students can expect to gain foundational training in statistics and data science as well as experience applying this training to business and marketing. The analytical skills you will develop are applicable to a wide range of disciplines.

Please contact educational advising for more information about the program. Business advising can answer questions about the BUAD courses included in the program. The Mathematics and Statistics department can answer questions about the MATH, STAT, or DSCI courses included in the program.

Data science diagnostic test

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  • Kelowna
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  • Full program offered
  • Partial program offered



Delivery options


  • International students eligible

Tuition and fees

2024-25: $9,952.51

Program details

This unique two-year post-baccalaureate diploma (60 credit/20 course) is aimed at students with a bachelor degree in any business or science program who wish to pursue a career in Marketing and Data Analytics. Students will receive thorough training in statistics and data science. Term one of this program sets the mathematical and statistical foundation for higher level learning in the marketing and data science area. In subsequent terms, students build on, and apply, these foundational skills to a diverse set of areas. While many of the applications have a business or marketing focus, the mathematical, statistical, and data science concepts learned are universally applicable to a wide range of disciplines.

Graduates of this program have the opportunity to work in a variety of fields:

  • Market Data Analyst
  • Agency Manager
  • Consultant
  • Marketing Insights Analyst
  • Data Scientist
  • Reporting Specialist

Students entering this program are expected to own their own laptop computer.  The computer should have Windows 10 installed  and with the following specs: 

  • A minimum of 8 GB of RAM.  

NOTE: If you can obtain 16 GB of RAM, this is a lot better. If 8GB is all you can obtain, then you will need to ensure you keep your stored files, and downloaded apps and programs to a minimum, and rely on external storage and cloud-based solutions. 

  •  For computing speed, you will require at least around 2+ GHz or better. 

NOTE: You can get away with 1.8-1.9 GHz, but speeds much slower might prove a little more time-consuming for you to complete assignment work, but can still function adequately most of the time.

  • An up-to-date, latest generation graphics card, that is optimized for speedy and optimal graphics rendering.  

NOTE: Many gaming laptops already have this. It is not necessary for you to use a gaming laptop, but we will be performing substantially graphics-heavy analyses for time to time, and the limitations of your graphics card can become an issue.  

You should ask your local computer outlet for assistance with the above, in the event that this is not clear to you, or if you have not purchased a laptop for advanced scientific studies before.  Also, although we recommend a PC with Windows 10, if you are running a different operating system (such as a MacOS, or an earlier version of Windows) you can still successfully complete the program.

Please be advised we perform our teaching activities exclusively in Windows 10 in this program, and this may mean that you could face challenges obtaining timely and/or effective assistance with issues that arise due to differences in our operating systems.  

Monitors: Many students have found it highly beneficial to invest in a second monitor for their laptop. This allows multiple screens and applications to be more easily viewed side-by-side when doing serious analysis work at home. In addition, having the ability to zoom effectively can greatly enhance clarity and visibility. We routinely make use of extended displays to greatly enhance our analysis and programming work, and we recommend that you at the very least investigate and decide whether this would be of benefit to you.

NOTE: Equipment requirements as of the 2021/22 academic year.

Students in the the Post-Baccalaureate Diploma in Marketing and Data Analytics come from backgrounds in Computer Science, Health, Math, Statistics and other fields. No matter the background, however, applicants should have interest and skills in Math in order to be successful in the program.

Campus Start date Schedule
Kelowna Jan. 08, 2024
Kelowna Sep. 04, 2024
Kelowna Jan. 06, 2025

Admission requirements

  • Successful completion of a recognized Bachelor Degree in any business or science program. A post-secondary basic calculus course, or equivalent, is highly recommended.
  • Applicants who have completed post-secondary studies outside of Canada will require a World Education Service evaluation with International Credential Advantage Package of their credentials.
  • A student who has completed a recognized undergraduate degree in a non-business or non-science program may be admitted to the program provided they pass the Okanagan College Basic Algebra Proficiency Test with a minimum score of 20/25 AND the Calculus Readiness Test with a minimum score of 16/25.

Program outline

Semester 1

DSCI 300 - Data Wrangling and Visualization
DSCI 310 - Mathematics Computation
BUAD 116 - Marketing
STAT 230 - Elementary Applied Statistics
MATH 314 - Calculus and Linear Algebra with Business Applications

Semester 2

Complete All of the following:
DSCI 400 - Machine Learning I
BUAD 123 - Management Principles
BUAD 200 - Digital Marketing
BUAD 210 - Introduction to Marketing Research
Complete at least 1 of the following:
STAT 240 - Applied Statistics II
DSCI 420 - Mathematics for Machine Learning

Semester 3

DSCI 401 - Machine Learning II
BUAD 283 - Management Information Systems
STAT 310 - Regression Analysis
BUAD 344 - Marketing Analytics and Data Analysis
Elective - any three credit academic course

Semester 4

STAT 311 - Modern Statistical Methods
BUAD 315 - Management Science
DSCI 490 - Data Science Project
Complete at least 1 of the following:
DSCI 351 - Discrete Structures for Data Science
MATH 251 - Introduction to Discrete Structures
Elective - any three credit academic course
  • Successful completion of the prescribed and elective courses as listed in the program outline with a minimum graduating grade average of 60%.
Additional information

View the official Calendar details and policies
Learn more about the department
View the Tuition and fees page


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