Business Problem Analysis and Modelling
  I: Administrative Information   II: Course Details   III: Topical Outline(s)  Printable Version
Section I: Administrative Information
  Total hours: 42.0
Credit Value: 3.0
Credit Value Notes: TBD
Effective: Fall 2021
Prerequisites: (INFO70278 AND INFO70279 AND INFO70280)
Corequisites: N/A
Equivalents: INFO70037
Pre/Co/Equiv Notes: The recommended program pathway is: INFO70278-Data Science Foundations INFO70279-Statistics for Data Science INFO70280-Data Cleansing INFO70281-Business Problem Analysis and Modelling

Program(s): Data Analyst
Program Coordinator(s): N/A
Course Leader or Contact: N/A
Version: 20210907_02
Status: Approved (APPR)

Section I Notes: Access to course materials and assignments will be available on Sheridan's Learning and Teaching Environment (SLATE). Students will need reliable access to a computer and the internet.

Section II: Course Details

Detailed Description
Students will examine the various stages of a business "use case" - the workflow of processes and tasks required to solve a business problem. From the perspective of a data analytics professional, students will examine a business use case, which includes constructing a business problem statement, translating the business problem into an analytics problem, selecting a model planning strategy, developing an analytical model, evaluating the model performance, and finally, deploying the model. Students will explore these concepts and practice applying the tools and techniques, as well as analyze real-life business problem analysis and modelling projects.

Program Context

Data Analyst Program Coordinator(s): N/A
Program: Data Analyst

Course Critical Performance and Learning Outcomes

  Critical Performance:
By the end of this course, students will have demonstrated the ability to apply the identified steps in the business problem analysis and modelling cycle, or business use case, to solve business problems.
Learning Outcomes:

To achieve the critical performance, students will have demonstrated the ability to:

  1. Construct a business problem statement that will yield data analytics results relevant to the problem.
  2. Convert a business problem into an analytics problem to define the scope of the data analyst's work and define the parameters of success.
  3. Identify different data types and data collection techniques to support the analytics process.
  4. Evaluate model planning strategies and select models appropriate to the problem being analyzed.
  5. Build and deploy an analytics model using various statistical techniques.
  6. Identify the different communication needs of various stakeholders regarding sharing of project results.
  7. Apply basic analytics problem techniques to business use cases from conception to conclusion to effectively address an identified business problem.

Evaluation Plan
Students demonstrate their learning in the following ways:

 Evaluation Plan: ONLINE
 Quiz 110.0%
 Quiz 215.0%
 Assignment 110.0%
 Assignment 215.0%
 Assignment 315.0%
 Assignment 420.0%
 Assignment 515.0%

Evaluation Notes and Academic Missed Work Procedure:
TEST AND ASSIGNMENT PROTOCOL The following protocol applies to every course offered by Continuing and Professional Studies. 1. Students are responsible for staying abreast of test dates and times, as well as due dates and any special instructions for submitting assignments and projects as supplied to the class by the instructor. 2. Students must write all tests at the specified date and time. Missed tests, in-class/online activities, assignments and presentations are awarded a mark of zero. The penalty for late submission of written assignments is a loss of 10% per day for up to five business days (excluding Sundays and statutory holidays), after which, a grade of zero is assigned. Business days include any day that the college is open for business, whether the student has scheduled classes that day or not. An extension or make-up opportunity may be approved by the instructor at his or her discretion.

Provincial Context
The course meets the following Ministry of Colleges and Universities requirements:


Essential Employability Skills
Essential Employability Skills emphasized in the course:

  • Critical Thinking & Problem Solving Skills - Use a variety of thinking skills to anticipate and solve problems.
  • Critical Thinking & Problem Solving - Apply a systematic approach to solve problems.
  • Information Management Skills - Analyze, evaluate, and apply relevant information from a variety of sources.
  • Information Management - Locate, select, organize and document information using appropriate technology and information systems.
  • Numeracy - Execute mathematical operations accurately.
  • Personal Skills - Manage the use of time and other resources to complete projects.
  • Personal Skills - Take responsibility for one's own actions, decisions, and consequences.

Prior Learning Assessment and Recognition
PLAR Contact (if course is PLAR-eligible) - Office of the Registrar

  • Not Eligible for PLAR

Section III: Topical Outline
Some details of this outline may change as a result of circumstances such as weather cancellations, College and student activities, and class timetabling.
Instruction Mode: Online
Professor: N/A
OptionalOtherRequired readings are all in the Sheridan library, and links will exist in Slate to those readings.

Applicable student group(s): FCAPS
Course Details:

Module 1: Introduction to Data Modelling

The Data Science Process and Modelling

Business Analysis Competencies

Conceptual Business Models

Common organizational applications

Frameworks for Data Analytics Projects

Information Action Value Chain, CoNVO Framework and CRISP-DM Framework

Quiz 1 – 10%



Module 2: Identifying a Business Problem

Framing a business problem

Requirements, Stakeholders, Problem Assessment

Refinement, Value and Cost

Assignment 1: Business Problem Framing: 10%



Module 3: Analytics Problem Framing

Steps in framing an analytics problem

Quality Function Deployment (QFD)

Kano’s model

House of Quality (HoQ)

Assignment 2: Analytics Problem Framing: 15%



Module 4: Data Collection

Data types

Data collection techniques

Sample Design

Sampling Plan

Quiz 2: Data Collection: 15%



Module 5: Model Planning

Prescriptive, Predictive and Descriptive Modelling

Methodology for Model selection

Selection Criteria

Statistics vs Machine Learning

Assignment 3: Model Planning:  15%



Module 6: Building a Model

Classification of Models

Data partitioning methods

Model structure and parameters

Optimization models

Linear programming models

Goal Programming

Non-Linear Programming

Assignment 4: Model Building (Linear Programming): 20%



Module 7: Model Evaluation and Case Study

Sources of degraded performance

Evaluation metrics

Models with probability outputs

Regression metrics

Application of Problem Analytics for Customer retention use case



Module 8: Model Deployment

Deployment tasks

Deployment Plan

Data and Model storage

Plan Monitoring

Model recalibration

Closing a Project

Assignment 5: Model Deployment and Case study: 15%

Sheridan Policies

All Sheridan policies can be viewed on the Sheridan policy website.

Academic Integrity: The principle of academic integrity requires that all work submitted for evaluation and course credit be the original, unassisted work of the student. Cheating or plagiarism including borrowing, copying, purchasing or collaborating on work, except for group projects arranged and approved by the professor, or otherwise submitting work that is not the student's own, violates this principle and will not be tolerated. Students who have any questions regarding whether or not specific circumstances involve a breach of academic integrity are advised to review the Academic Integrity Policy and procedure and/or discuss them with the professor.

Copyright: A majority of the course lectures and materials provided in class and posted in SLATE are protected by copyright. Use of these materials must comply with the Acceptable Use Policy, Use of Copyright Protected Work Policy and Student Code of Conduct. Students may use, copy and share these materials for learning and/or research purposes provided that the use complies with fair dealing or an exception in the Copyright Act. Permission from the rights holder would be necessary otherwise. Please note that it is prohibited to reproduce and/or post a work that is not your own on third-party commercial websites including but not limited to Course Hero or OneNote. It is also prohibited to reproduce and/or post a work that is not your own or your own work with the intent to assist others in cheating on third-party commercial websites including but not limited to Course Hero or OneNote.

Intellectual Property: Sheridan's Intellectual Property Policy generally applies such that students own their own work. Please be advised that students working with external research and/or industry collaborators may be asked to sign agreements that waive or modify their IP rights. Please refer to Sheridan's IP Policy and Procedure.

Respectful Behaviour: Sheridan is committed to provide a learning environment that supports academic achievement by respecting the dignity, self-esteem and fair treatment of every person engaged in the learning process. Behaviour which is inconsistent with this principle will not be tolerated. Details of Sheridan's policy on Harassment and Discrimination, Academic Integrity and other academic policies are available on the Sheridan policy website.

Accessible Learning: Accessible Learning coordinates academic accommodations for students with disabilities. For more information or to register, please see the Accessible Learning website (Statement added September 2016)

Course Outline Changes: The information contained in this Course Outline including but not limited to faculty and program information and course description is subject to change without notice. Any changes to course curriculum and/or assessment shall adhere to approved Sheridan protocol. Nothing in this Course Outline should be viewed as a representation, offer and/or warranty. Students are responsible for reading the Important Notice and Disclaimer which applies to Programs and Courses.

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