Hands-on Data Analytics Skills
 

NIHI - McMaster CE Microcredential Course

 

 

Instructor
Trevor Strome, MSc,
Manager, Digital Airport Solutions, Winnipeg Airports Authority; Assistant Professor, Department of Emergency Medicine, College of Medicine, Faculty of Health Sciences, University of Manitoba; NIHI Instructor; Author "Healthcare Analytics for Quality and Performance Improvement"

 

The goal of this course is to provide skilled workers knowledge to support the increasing data analytics demands of business, such as health care, across Canada. We use the most popular programming language, Python, to guide students in a step by step process. As well, we will use online "notebooks" that will allow participants to work on their code without needing to install Python on their own computers. Through a combination of live instruction and hands-on exercises, we will focus on the application of data tools for data analysis in a practical environment.

Upon completion you will be awarded a NIHI - McMaster CE microcredential in Data Analytics: Hands-on Data Analytics Skills. A microcredential is issued in a digital format, provides details of acquired competencies and is shareable and transportable.

Please note: 

• You do not need to take the NIHI - McMaster University CE Courses called Data Analytics I or Data Analytics II in order to register for this course. 
• You do not need to have any prior experience with the Python programming language or download the software to your computer. 
   

Module Plan

• ​Session 1 - Course Overview and Introduction - 1.5 Hour
   
• Session 2 (Recorded) - Introduction to Python 
  • Using the online notebooks
  • Basic Python programming overview
     
• Session 3 (Recorded) - Data Structures
  • Loading, cleaning and manipulating data in Python 
     
• Session 4 (Recorded) - Descriptive Statistics 
  • Analyzing and visualizing data using descriptive statistics
     
• Session 5 - Content Review, Q&A, Assignment Discussion  - 1.5 Hour
   
• Session 6 (Recorded) - Advanced Analytics 
  • Building predictive models in Python, for example:
    • Regression
    • Decision-trees
    • Neural Networks
       
• Session 7 (Recorded) - Fitting In
  • Getting Python to work with your organization's analytics ecosystem. 
     
• Session 8 - Course summary, Q&A  - 1.5 Hour

Competencies Achieved

• Apply basic Python programming skills.
• Analyze and visualize data using descriptive statistics.
• Build predictive models in Python.
• Examine steps to apply Python to an organization's analytics ecosystem.

Intended Audience

• Anyone responsible for developing or utilizing analytics in public or private sector organizations
• Chief Analytics Officers
• Chief Data Officers
• Chief information officers
• Data Scientists
• Business Intelligence Analysts
• Data Analysts
• Policy Analysts
• Systems / Business Analysts
• Database Administrators
• Consultants and solution providers who need to offer analytics-related products and services to private and public sector organizations
• Technical architects and developers who design and build cloud, mobile, and other analytical solutions.