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: N/A
Effective: Fall 2021
Prerequisites: N/A
Corequisites: N/A
Equivalents: INFO70281
Pre/Co/Equiv Notes: N/A

Program(s): Data Science
Program Coordinator(s): N/A
Course Leader or Contact: N/A
Version: 20210907_00
Status: Approved (APPR)

Section I Notes: This is a course offered through OntarioLearn. Access to the course materials will be through OntarioLearn.com and you will be sent an email with your login details.

Section II: Course Details

Detailed Description
Students learn the steps in the business analytics model cycle including transforming a business problem into an analytics problem, collecting and preparing data for analysis, building an analytic model and deployment. Students use software for data preparation and analysis. NOTE: For students who require this course for completion of the Data Science Board Certificate, please note that you can complete the equivalent course INFO70281 Business Problem Analysis and Modelling.

Program Context

Data Science	Program Coordinator(s): N/A
This is a mandatory course in the Data Science Certificate.

Course Critical Performance and Learning Outcomes

	Critical Performance:
	By the end of this course, students will have demonstrated the ability to apply the data modeling cycle to business applications.

	Learning Outcomes: To achieve the critical performance, students will have demonstrated the ability to:
	Construct a business problem statement suited to a data analytics solution Convert a business problem to an analytics problem Prepare data for analysis Select a model planning strategy Build an analytic model Deploy an analytic model Apply the modeling cycle to business problems

Evaluation Plan
Students demonstrate their learning in the following ways:

Evaluation Plan: ONLINE

	Quizzes (2 x 5 %)	10.0%
	Assignment 1	10.0%
	Assignment 2	10.0%
	Assignment 3	15.0%
	Assignment 4	10.0%
	Assignment 5	15.0%
	Assignment 6	15.0%
	Assignment 7	15.0%
	Total	100.0%

Evaluation Plan: IN-CLASS

	Quizzes (2 x 5 %)	10.0%
	Assignment 1	10.0%
	Assignment 2	10.0%
	Assignment 3	15.0%
	Assignment 4	10.0%
	Assignment 5	15.0%
	Assignment 6	15.0%
	Assignment 7	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 the Faculty of 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:

Prior Learning Assessment and Recognition
PLAR Contact (if course is PLAR-eligible) - Office of the Registrar
Students may apply to receive credit by demonstrating achievement of the course learning outcomes through previous relevant work/life experience, service, self-study and training on the job. This course is eligible for challenge through the following method(s):

Challenge Exam
Notes:
Portfolio
Notes:

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.

Print	Instruction Mode	Professor	Applicable Student Group(s)
	In-Class	N/A	Continuing and Professional Studies Students: On-Campus
	Online	N/A	Continuing and Professional Studies Students: Online

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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