NOTE: For registration please contact Bob Gaglardi School of Business and Economics
General Program Inquiries (Email: email@example.com or Tel:
Students learn to integrate personal judgment and intuition in realistic business situations
with the most widely applicable methodologies of decision and risk analysis, probability and
statistics, competitive analysis, and management science. Topics include an introduction to
decision analysis and modelling; spreadsheet engineering and error reduction; framing decision
analysis problems; framework for analyzing risk; data analysis; resource allocation with
optimization models; multi-period deterministic models; multi-factor deterministic models;
regression modelling; strategic interactive decisions; and interpreting models, data, and
- Describe the range of cognitive, psychological, and social pitfalls which decision makers
- Critically evaluate decisions of others and develop ways they could have improved their
- Demonstrate translating descriptions of decision problems into formal models, and investigate
those models in an organized and systematic fashion.
- Illustrate best practice modelling techniques such as the FAST modelling standard, strategies
for reducing errors, and other methods to ensure consistent and easy to understand models.
- Demonstrate how analytical techniques and statistical models can help enhance decision making
by converting data to information and insights for decision making.
- Categorize and construct multistage decision analysis problems using decision trees.
- Categorize and construct multifactor problems with multiple objectives and uncertainty.
- Demonstrate linear, non-linear, and goal programming models for resource allocation and
- Critically evaluate various short-term forecasting and regression models and identify their
appropriate use and limitations.
- Effectively use online data sources and Internet resources to access necessary information
for model development.
- Interpret model results in the context of the business situation and address the inherent
assumptions through sensitivity and scenario analysis.
- Effectively communicate the analysis, outcomes, assumptions, and implications through
spreadsheet models and presentations.
- Module 1: Introduction to Decision-Making
- Module 2: Spreadsheet Engineering and Error Reduction
- Module 3: Data Analysis - Descriptive Statistic
- Module 4: Forecasting - Predicting the Future
- Module 5: Modelling - Framing Decision Analysis Problems
- Module 6: Simulation - A Framework for Analyzing Risk
- Module 7: Resource Allocation - Optimization Models
- Module 8: Decision Trees - Multi-Period Decisions
- Module 9: AHP and DEA - Group Decision-Making Tools
- Module 10: Data ELT
- Module 11: Dashboards and Visualization
- Module 12: Data and Decisions – A Management Consultant Perspective
- Module 13: Practice Exam
Required text and materials
Online students are responsible for sourcing and ordering their own textbooks. Please see the
list of required textbooks here: https://www.tru.ca/distance/courses/MBA_Textbook_List.pdf.
Please note that publishers may offer several package options that include additional resource
material not required in your course. You may purchase a package of your choice as long as it
includes the correct author, title and edition listed for your course.
If you have any questions about obtaining the correct textbook, please contact OLMaterials@tru.ca. They will be happy to assist you.
- A computer with Internet access
- Excel 2019 (If you have a Windows- or Mac-based machine, then Excel 2016 is
Note: If you have a Mac you MUST have Excel 2016 or later and be prepared to
do a lot of research into Apple’s version of functions and short-cut keys. We will be using a
series of free Excel add-ins in this course (Decision Trees, Tornado Charts, etc.), and be aware
that some of these add-ins may not work on a Mac.
Please be aware that should your course have a final exam, you are responsible for the fee to the online proctoring service, ProctorU, or to the in-person approved Testing Centre. Please contact firstname.lastname@example.org with any questions about this.
To successfully complete this course, students must achieve a passing grade of 70% or higher on
the overall course, and 50% or higher on the final mandatory examination.
|Discussion 1 – HR Analytics
|Discussion 2 – Marketing Analytics
|Discussion 3 – Accounting Analytics
|Discussion 4 – Supply Chain Analytics
|Assignment 1 - Mind Map
|Assignment 2 – TRU Sports
|Assignment 3 – Description Stats
|Assignment 4 – Forecasting
|Assignment 5 – Bob’s Retirement
|Assignment 6 – TRU Coffee
|Assignment 7 – Optimization Models
|Assignment 8 – Decision Trees
|Assignment 9 – AHP
|Assignment 10 – Data Preparation
|Assignment 11 – Dashboards
|Assignment 12 – Consulting
|Final Exam (mandatory)
Open Learning Faculty Member Information
An Open Learning Faculty Member is available to assist students. Students will receive the necessary contact information at the start of the course.