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: Winter 2020
Prerequisites: N/A
Corequisites: N/A
Equivalents: N/A
Pre/Co/Equiv Notes: N/A

Program(s): Data Science
Program Coordinator(s): N/A
Course Leader or Contact: N/A
Version: 20200106_00
Status: Approved (APPR)

Section I Notes: Post-secondary education in a field related to computer science or mathematics is recommended. Alternatively, students would benefit from a basic understanding of programming concepts. This course is offered face-to-face and online. For the face-to-face course, the sessions may include a variety of interactive and engaging activities including discussions, group activities, case studies and presentations. Readings, video, and podcasts may be provided online, on Sheridan's Learning and Teaching Environment (Slate), to support class activities and reinforce material covered during class sessions. Assignment details will be provided in class and on Slate. For the online course, all instruction is delivered through a Learning Management System. For the online course only, there will be no face-to-face meetings. Online learning often involves assigned weekly readings, research, assignments, quizzes, and discussion forums. Some courses may include live online class sessions which will involve learner participation. Students will need reliable access to a computer and the internet.

Section II: Course Details

Detailed Description
Students learn concepts and techniques for data mining. Methods for the application of various probability distribution techniques are used to evaluate the probability of real world events. Students learn about logistic and multiple regression and how that is used in making predictions. Pattern evaluation methods are explored for marketing data.

Program Context

Data Science	Program Coordinator(s): N/A
This course is part of 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 statistical techniques for the purpose of analysis, visualization and interpretation of various types of data.

	Learning Outcomes: To achieve the critical performance, students will have demonstrated the ability to:
	Use statistical techniques for data mining. Perform multiple regression analysis Use logistic regression to analyze data Perform cluster analysis on data Apply Association analysis to data Calculate non-parametric statistics Perform categorical Data Analysis

Evaluation Plan
Students demonstrate their learning in the following ways:

Evaluation Plan: ONLINE

	Quizzes (4 x 10%)	40.0%
	Assignment 1	25.0%
	Assignment 2	15.0%
	Assignment 3	20.0%
	Total	100.0%

Evaluation Notes and Academic Missed Work Procedure:

N/A

Evaluation Plan: IN-CLASS

	Quizzes (4 x 10%)	40.0%
	Assignment 1	25.0%
	Assignment 2	15.0%
	Assignment 3	20.0%
	Total	100.0%

Evaluation Notes and Academic Missed Work Procedure:

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: In-Class Students
	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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