Highlights

Here are some of the highlights for this micro-credential.

  • No admission requirements, no application process, no pre-requisites, open to anyone

  • Take up to 90 days to complete the course

  • Learn at your own pace using a computer, tablet or smartphone

  • Receive a certificate of course completion and digital badge for successfully completing the course which can be shared on social media (e.g. LinkedIn, Facebook, Twitter), downloaded as a PDF, or even embedded in an email signature

Overview

This 20-hour online and self-directed micro-credential on Predictive Health Analytics provides a comprehensive introduction to the principles, methods, and applications of predictive analytics in healthcare. This course equips learners with the technical and analytical skills required to utilize data-driven insights, improve patient outcomes, and support evidence-based decision-making in healthcare settings.

By engaging in real-world case studies and discussions, learners will gain proficiency in statistical analysis, machine learning techniques, and ethical considerations related to predictive analytics in healthcare.

Learners who successfully complete the course will earn a digital badge and certificate of course completion that can be downloaded and shared via social media. 

You will have access to the course for 90 days to work through the content.

Knowledge, Skills, & Abilities

Upon successful completion of this micro-credential,, your should be able to:

  • Define key concepts and terminology related to healthcare analytics.

  • Describe the role of data exploration and statistical methods in predictive healthcare analytics.

  • Apply statistical techniques to analyze healthcare data and derive meaningful insights. ea

  • Examine healthcare datasets using statistical methods to identify patterns and trends.

  • aAssess the effectiveness of predictive models in healthcare decision-making and patient outcomes.

  • Develop a predictive healthcare analytics solution using machine learning techniques while addressing ethical and regulatory considerations.

Curriculum

    1. Land Acknowledgement

    2. Accessibility, Diversity, Equity, and Inclusion (IDEA) Statement

    3. Introduction from Sault College's eLearning Department

    4. What is asynchronous online self-directed learning?

    5. Learner Resources and Supports

    6. Technology Requirements

    7. Privacy and Accessibility Policies

    8. Navigation

    9. Learner Introductions

    10. Discussion Forum: Learner Engagement

    11. Technical Discussion Forum

    12. Frequently Asked Questions

    13. Help & Contact Information

    1. Before we begin...

    2. Welcome and Micro-credential Information

    3. Micro-credential Overview

    4. Learning Outcomes & Goals

    5. Modes of Learning

    1. Module 1: Introduction Part A

    2. Module 1: Introduction Part B

    3. Project R Downloadable File

    4. Quiz 1

    5. Quiz 2

    6. Quiz 3

    7. Quiz 4

    8. Quiz 5

    9. Quiz 6

    10. Quiz 7

    11. Quiz 8

    12. Quiz 9

    1. Module 2: Data Exploration and Preparation

    2. Module 2: Discussion

    3. Quiz 1

    4. Quiz 2

    5. Quiz 3

    6. Quiz 4

    1. Module 3 : Statistical Methods for Predictive Analytics

    2. Module 3: Discussion

    3. Quiz 1

    4. Quiz 2

    5. Quiz 3

    6. Quiz 4

    7. Quiz 5

    1. Module 4 : Machine Learning Techniques for Predictive Analytics

    2. Quiz 1

    3. Quiz 2

    4. Quiz 3

    5. Quiz 4

    6. Quiz 5

About this course

  • $289.99