INFO70041
Predictive Analytics and Machine Learning		 Sheridan
 
  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: N/A
Effective: Fall 2018
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: 20180904_04
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, and case studies. 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 are introduced to various machine learning concepts and techniques. Through the application of python libraries, students learn to create and evaluate supervised and unsupervised machine learning models. Students also implement predictive models for diverse data types including natural language.

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 build machine learning models to make predictions on structured and unstructured data.
 
Learning Outcomes:

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

  1. Explain machine learning concepts
  2. Apply python programming concepts to data science problems
  3. Apply Python data science libraries to machine learning problems.
  4. Make predictions using machine learning models based on regression techniques
  5. Make predictions using supervised learning algorithms
  6. Make predictions using unsupervised learning algorithms
  7. Make predictions using machine learning models based on natural language processing techniques

Evaluation Plan
Students demonstrate their learning in the following ways:

 Evaluation Plan: ONLINE
 Assignment 115.0%
 Assignment 215.0%
 Assignment 320.0%
 Assignment 420.0%
 Assignment 520.0%
 Quiz (2 x 5%)10.0%
Total100.0%



Evaluation Plan: IN-CLASS
 Assignment 115.0%
 Assignment 215.0%
 Assignment 320.0%
 Assignment 420.0%
 Assignment 520.0%
 Quiz (2 x 5%)10.0%
Total100.0%

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

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

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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