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Course Description

Female IT engineer standing in front of a data center

This course will equip you with the knowledge and skills on the use of algorithms and their application in different domains. You will discover how to visualize the results of data analysis and present the results in a way that tells a story and explains concepts using non-technical language. The course will be delivered using a variety of hands-on learning activities that provide participants with practice scenarios in data mining and machine learning.

This course is designed for professionals working with data from various sectors in healthcare, finance, insurance and marketing, data science and data analytics professionals.

If you have previous experience coding in R, you may be eligible for advanced standing in the Data Science Certificate program. For additional information about completing a prior learning assessment, please contact the program manager

Learning Outcomes

Upon successful completion of the course, students will be able to:

  • Examine cases of descriptive and predictive problems.
  • Apply clustering approaches such as K-means and hierarchical clustering.
  • Investigate data classification using statistical methods such as logistic regression, naïve Bayes.
  • Demonstrate machine learning with Support Vector Machines, decision trees and random forest.
  • Explain the Deep Learning concept and methodologies and apply Neural Network methods to data sets.
  • Describe model evaluation methods.
  • Discover how to visualize the results of data analysis and present results in non-technical language.
  • Program using advanced R language.

Additional Requirements

To be successful in this program, it is recommended that you have an Undergraduate degree or College diploma, a recent statistics course and basic experience or understanding of a programming language.

Applies Towards the Following Certificates

Technical Requirements

You are responsible for ensuring that your computer system meets the necessary system requirements. Use the browser check tool to ensure your browser settings are compatible and up to date (results will be displayed in a new browser window).

*Course details are subject to change.

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Enrol Now - Select a section to enrol in

Section Title
Data Mining and Machine Learning
Type
Online
Dates
January 18, 2021 to March 14, 2021
Contact Hours
40.0
Delivery Options
Online  
Course Fee(s)
Tuition Fee $1,050.00
Potential Discount(s)
Instructors
Section Notes

Course registration closes Monday, January 24, 2021.

You may purchase the required textbook from a bookseller of your choice.

View the Withdrawal, Transfer, and Refund Policy

Contact our main office if you require assistance.

Section Materials
  • Textbook (Confirmed) (Mandatory) An Introduction to Statistical Learning with Applications in R by James, G., Witten, D., Hastie, T., Tibshirani, R. © 2017 Springer-Verlag, New York 2017 edition ISBN 9781461471370 Springer-Verlag, New York
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