INFO70042
Data Science Applied Project		 Sheridan
 
  I: Administrative Information   II: Course Details   III: Topical Outline(s)  Printable Version
 

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 2020
Prerequisites: N/A
Corequisites: N/A
Equivalents: N/A
Pre/Co/Equiv Notes: Recommended Prerequisites: MGMT70045; INFO70037; INFO70038; INFO70039; INFO70040; INFO70041

Program(s): Data Science
Program Coordinator(s): N/A
Course Leader or Contact: N/A
Version: 20200914_00
Status: Approved (APPR)

Section I Notes: The Spring 2020 course will run in a Virtual format using Video Conference Technology through SLATE (Sheridan Learning and Training Environment). This approach will allow for real-time, synchronous learning, in the same manner that students experience in the classroom. Course materials and assignments will be provided on Sheridan's Learning and Teaching Environment (SLATE). The In-Class evaluation plan will be applicable for this specific course. Students will need reliable access to a computer and the internet.
 
 
Section II: Course Details

Detailed Description
In the field of data science, the ability to craft sound recommendations and propose business strategies based on small or large data sets is essential. Students work through a full data cycle to verify data, develop visualizations of multivariate data, and construct predictive analytics visualizations. In this project-based course, students perform exploration, analysis and prediction processes while using appropriate visualization, reporting techniques and best practices to present findings and recommendations to stakeholders. This is a capstone course and therefore intended to be the last course that students complete in the program.

Program Context

 
Data Science Program Coordinator(s): N/A
This is a required capstone course in the Data Science Certificate, where students synthesize learning from across the program in a final culminating project.


Course Critical Performance and Learning Outcomes

  Critical Performance:
By the end of this course, students will have demonstrated the ability to communicate recommendations and business strategies based on the analysis of a real-world business problem.
 
Learning Outcomes:

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

  1. Formulate a business problem statement suited to a data analytics solution.
  2. Select appropriate research methodologies needed for specific statistics and machine learning scenarios.
  3. Apply ethical principles in the collection and management of data.
  4. Prepare data for analysis using cleaning and visualization techniques.
  5. Analyze business problems through predictive modelling using an appropriate statistical approach.
  6. Solve big data computation problems using big data tools.
  7. Build interactive dashboards and data stories in Tableau.
  8. Communicate analytical findings through visual and oral presentation.

Evaluation Plan
Students demonstrate their learning in the following ways:

 Evaluation Plan: ONLINE
 Capstone Deliverable 1: Capstone Proposal15.0%
 Capstone Deliverable 2: Cleaning and Visualization Report15.0%
 Capstone Deliverable 3: Feature Selection10.0%
 Capstone Deliverable 4: Predictive Modeling20.0%
 Capstone Deliverable 5: Visualization with Dashboard10.0%
 Capstone Final Deliverable, Presentation10.0%
 Capstone Final Deliverable, Report20.0%
Total100.0%

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.



Evaluation Plan: IN-CLASS
 Class Participation15.0%
 Capstone Deliverable 1: Capstone Proposl10.0%
 Capstone Deliverable 2: Cleaning and Visualization Report20.0%
 Capstone Deliverable 3: Predictive Modeling15.0%
 Capstone Deliverable 4: Visualization with Dashboard10.0%
 Capstone Final Deliverable, Presentation10.0%
 Capstone Final Deliverable, Report20.0%
Total100.0%

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:

 

Essential Employability Skills
Essential Employability Skills emphasized in the course:

  • Communication Skills - Communicate clearly, concisely and correctly in the written, spoken, visual form that fulfills the purpose and meets the needs of the audience.
  • Communication Skills - Respond to written, spoken, or visual messages in a manner that ensures effective communication.
  • 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: In-Class
Professor: N/A
Resource(s): N/A
Applicable student group(s): Continuing and Professional Studies Students
Course Details:

Module 1: Business Problem Plans

  • Data Science Life cycle 
  • Identifying business problems 
  • Solving business problems

Capstone Deliverable 1: Capstone Proposal (10%)

 

Module 2: Research Methodology and Data Science

  • Research Problem 
  • Hypothesis Testing
  • Types of Research Methods
    • Quantitative vs. Qualitative research methods
    • Inductive vs. Deductive Approach
  • Measurability 

Module 3: Ethics and Data Science 

  • The Canadian Legal Framework:
    • The Personal Information Protection and Electronic Document Act (PIPEDA)
    • The Freedom of Information and Protection of Privacy Act (FIPPA)
  • Ethical principles and Capstone project 

Module 4: Data Wrangling and Exploratory Data Analysis (EDA) 

  • Data sources and structure
  • Data preparation for modeling
  • Data cleaning techniques in OpenRefine or DataWrangler
  • Visualizing and investigating data in R and Tableau

Capstone Deliverable 2: Data Cleaning and Visualization Report (20%)

 

Module 5: Statistical Approaches and Predictive Model 

Linear Regression Models and Extensions

  • Feature selection methods
  • Dimension Reduction
    • Principal Component Analysis
    • Principal Component Regression 
  • Partial Least Square Regression 
  • Shrinkage Methods
    • LASSO
    • Ridge regression
  • Challenges with regression 

Non-Linear Regression & Learning Trees

  • Building Extensions
    • CART (Classification & Regression Tree)
    • Polynomial regression
    • Multivariate Adaptive Regression Splines

Capstone 3: Predictive Modeling (15%)

 

Nonlinear Classification Models

  • k-NN
  • Naïve Bayes

Module 6: Parallel Computing and Big Data Platforms

  • Big Data computational challenges
  • KNIME
  • Spark 

Module 7: Interactive Dashboards and Data Stories

  • Principles for constructing data stories
  • Dashboards and multiple visualizations
  • Filters and actions for dashboards

Capstone Deliverable 4: Visualization with Dashboard (10%)

 

Module 8: Prepare Report and Presentation.

  • Key elements of a written report
  • Formatting a written report
  • Key elements of an oral presentation

Capstone Final Deliverable: Project Presentation (10%), Project Report (20%)

 

Class participation: 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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