INFO70278
Data Science Foundations
Sheridan
 
  I: Administrative Information   II: Course Details   III: Topical Outline(s)  Printable Version
 
Section I: Administrative Information
  Total hours: 15.0
Credit Value: 1.0
Credit Value Notes: N/A
Effective: Fall 2021
Prerequisites: N/A
Corequisites: N/A
Equivalents: N/A
Pre/Co/Equiv Notes: N/A

Program(s): Data Analyst
Program Coordinator(s): N/A
Course Leader or Contact: N/A
Version: 20210907_00
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
This course offers the opportunity to engage in class challenges, apply coding concepts, and experience hands-on learning as students work through real examples. Students explore the basic aspects of probability and statistics, and learn to program in the open software programming language, R. Students apply a combination of statistics and data visualization into their first regression and analyze its impact.

Program Context

 
Data Analyst Program Coordinator(s): N/A
This is an introductory course in the Data Analyst micro-credential that forms a foundational knowledge-base for subsequent courses.


Course Critical Performance and Learning Outcomes

  Critical Performance:
By the end of this course, students will have demonstrated the ability to create and perform statistical mathematical calculations, practice using R programming language, understand data science career options and the corresponding program stream aligned with that specialization, and uncover their own career interests.
 
Learning Outcomes:

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

  1. Explain the difference between probability and statistics in data science.
  2. Summarize the statistical enquiry cycle and define each stage.
  3. Explain and apply the concepts and steps in performing a regression.
  4. Apply and interpret R for basic calculations in data science.
  5. Describe data science career specializations.
  6. Identify an area of interest, and the program stream that provides the required knowledge and skills to work in that field.

Evaluation Plan
Students demonstrate their learning in the following ways:

 Evaluation Plan: ONLINE
 Final Quiz100.0%
Total100.0%

Evaluation Notes and Academic Missed Work Procedure:
Grade of 60% is required to pass the quiz. All projects and exercises must be submitted on or before the date and time specified in the project brief. Late assignments will receive a 10% grade reduction per day. If there is a valid reason for the late project, the student must notify the professor no later than the date the project is due to indicate it will be late. If the reason is deemed to be valid, there will be no late penalty. Work more than one week late (on the 8th day after the due date) will not be graded unless a prior arrangement has been made with the instructor. There will be no resubmission of work unless, under exceptional circumstances, this has been agreed to or suggested by the instructor.

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 - 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.
  • 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.
  • 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
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:  Both a challenge exam and portfolio are required.
  • Portfolio
    Notes:  Both a challenge exam and portfolio are required.

 
 
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):
 TypeDescription
OptionalOtherNo textbook required.

Applicable student group(s): FCAPS
Course Details:

Module 1: Introduction to Data Science

  • The Data Science Process
  • Data Science as a Career
  • Data Science Specializations and Program Options
  • Career Quiz

 

Module 2: Probability and Statistics

  • Overview of probability and distributions
  • Overview of binomial and normal distributions
  • Examination of the statistical enquiry cycle
  • Variability measures

 

Module 3: Programming in R

  • Launch and get rolling with R
  • Basic stat computations in R
  • Working example using the US Arrests dataset

 

Module 4: Introduction to Regressions

  • Regression review
  • Identifying the response variable
  • Creating and interpreting plots
  • Creating and evaluating a regression

Final Quiz 100%

 



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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