Sheridan Course Outlines

Land Acknowledgement

Sheridan College resides on land that has been, and still is, the traditional territory of several Indigenous nations, including the Anishinaabe, the Haudenosaunee Confederacy, the Wendat, and the Mississaugas of the Credit First Nation. We recognize this territory is covered by the Dish with One Spoon treaty and the Two Row Wampum treaty, which emphasize the importance of joint stewardship, peace, and respectful relationships.

As an institution of higher learning Sheridan embraces the critical role that education must play in facilitating real transformational change. We continue our collective efforts to recognize Canada's colonial history and to take steps to meaningful Truth and Reconciliation.

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:

Construct a business problem statement that will yield data analytics results relevant to the problem.
Convert a business problem into an analytics problem to define the scope of the data analyst's work and define the parameters of success.
Identify different data types and data collection techniques to support the analytics process.
Evaluate model planning strategies and select models appropriate to the problem being analyzed.
Build and deploy an analytics model using various statistical techniques.
Identify the different communication needs of various stakeholders regarding sharing of project results.
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 1	10.0%
	Quiz 2	15.0%
	Assignment 1	10.0%
	Assignment 2	15.0%
	Assignment 3	15.0%
	Assignment 4	20.0%
	Assignment 5	15.0%
	Total	100.0%

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
Resource(s):

	Type	Description
Optional	Other	Required 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

It is recommended that students read the following policies in relation to course outlines:

Academic Integrity
Copyright
Intellectual Property
Respectful Behaviour
Accessible Learning

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

Appropriate use of generative Artificial Intelligence tools: In alignment with Sheridan's Academic Integrity Policy, students should consult with their professors and/or refer to evaluation instructions regarding the appropriate use, or prohibition, of generative Artificial Intelligence (AI) tools for coursework. Turnitin AI detection software may be used by faculty members to screen assignment submissions or exams for unauthorized use of artificial intelligence.

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. 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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