Leadership in Patient-Oriented Research: Data Analytics for Health Research

Data Analytics for Health Research Overview

The Data Analytics for Health Research course is designed to enable current professionals in the field of health data analytics to use administrative data effectively and to build the capacity in the Alberta workforce and research environment for the use of big data. At the end of the certificate program the learners will be able to:

  1. Develop and apply knowledge of databases and administrative data in the context of health research through a case study
  2. Perform basic analysis, including decision-making about the steps to take in the process of addressing a research question
  3. Demonstrate understanding of data features, including defining study populations and variables
  4. Explain the principles of data quality in administrative health data, identify and apply methods for assessing data quality, and articulate the impact of data quality on results
  5. Demonstrate the ability to employ analytical techniques, including when specific tools/strategies are appropriate based on the research question and available data
  6. Develop an individual research question and present a plan for how this research question could be addressed utilizing the steps, processes, and techniques from the weekly sessions

Content

The content of the Data Analytics for Health Research course is designed to integrate introductory topics on health data and analytics, while providing additional content, learning strategies, and practical application exercises using real-world administrative data examples. 

Data Access

The course has been designed to use existing data sets to answer a clinical research question and has used both Alberta-based and US data. The data set utilized for upcoming courses will depend on the availability and access structures at the time of the course offering. This information will be included in upcoming course materials.

Course Structure

A research question will be used as a case study as the foundation of applying analytical skills; preceptors will use this question as an example each week and learners will apply these skills in individual and group work during the class time.

Requirements

In the course application, applicants must demonstrate:

  • Basic biostatistics knowledge (such as descriptive analysis, frequency analysis, and regression modelling)
  • Basic knowledge of statistical software
  • Basic epidemiological knowledge

Pre-requisites

  1. Attend the Intro to Data for Health Research event at either the University of Calgary or University of Alberta. Details and registration info will be available timely.
  2. Complete the following external courses:
    1. Python for Everybody: https://www.py4e.com/

For learners with no experience, this course is required. Learners with some coding experience may want to review specific modules as needed prior to completing the Intro to Data Science in Python course.

Course Expectations

Accepted applicants are expected to:

  • Complete the pre-requisite courses prior to the first day of classes
  • Attend weekly in-person sessions at either the University of Calgary or University of Alberta
  • Bring a laptop with R statistical software installed 
  • Participate in tutorial sessions, including group work activities
  • Develop a hypothetical research question for use in the final assignment of the program

Workload

This is an 8-week Certificate program. Learners may spend 3-4 hours per week. Individual time spent on the final assignment may vary, but in-class support will be provided. 

Format

The certificate is offered in-person with weekly sessions over 8 weeks. Weekly modules will include a brief presentation on various topics related to the analytical processes essential for research involving administrative health data, followed by a tutorial session to apply the material to a case study. Some lectures may be recorded for review prior to in-person sessions with preceptors for hands-on tutorials using the administrative data. 

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