PHDA - Admission criteria and fees
Admission criteria
Certificate students apply, and are admitted to the program for non-credit study (application to the university is not required). The certificate requires completion of four courses, including completion of core and proven proficiency courses. Students successfully completing the program requirements will be granted a professional specialization certificate by the University of Victoria.
Looking for professional development credits for PHDA courses?
Currently, the Canadian Health Information Management Association (CHIMA) and the Canadian Institute of Public Health Inspectors (CIPHI) recognize our PHDA courses for 10 CPE credits per course.
See also: PHDA and Core Competencies for Public Health Professionals
Our courses provide the practical skills you need to enhance your work and research. See what our instructors are saying about our courses.
PHDA and Core Competencies for Public Health Professionals
The Core Competencies for Public Health Professionals are a consensus set of foundation skills for the broad practice of public health. The Core Competencies for Public Health in Canada are organized into seven skill areas or domains that transcend the boundaries of specific disciplines and provide a baseline for all public health professionals.
The seven categories include:
Meet the team of PHDA Instructors
PHDA 01 Working with Administrative Data
Education and training
The Education and Training Unit (ETU) is an analysis training centre that supports research on human health, well-being and development, and responds to the training and education needs of researchers and population health professionals.
In this section of the website you will find information on:
The Professional Specialization Certificate in Population Health Data Analysis
This professional certificate provides a unique opportunity to learn a diverse set of skills from multiple disciplines.
Intro to Data Science Module 4: Advanced Unsupervised Learning (Session 2)
This webinar is part of the Introduction to Data Science Webinar Series
Practicum session
- Who uses unsupervised learning?
- K-means
- Expectation-maximization
- Susceptibility to outliers
- Dangers of labeling clusters
Watch recorded presentation below.
Intro to Data Science Module 4: Advanced Unsupervised Learning (Session 1)
This webinar is part of the Introduction to Data Science Webinar Series
Introductory session
- Who uses unsupervised learning?
- K-means
- Expectation-maximization
- Susceptibility to outliers
- Dangers of labeling clusters
Watch recorded presentation below.
Intro to Data Science Module 3: Advanced Supervised Learning (Session 2)
This webinar is part of the Introduction to Data Science Webinar Series
Practicum session
- Decision trees
- Problems in overfit
- Random Forest
- Out-of-bag error vs cross-validation
Watch recorded presentation below.
Intro to Data Science Module 3: Advanced Supervised Learning (Session 1)
This webinar is part of the Introduction to Data Science Webinar Series
Introductory session
- Decision trees
- Problems in overfit
- Random Forest
- Out-of-bag error vs cross-validation
Watch recorded presentation below.
Intro to Data Science Module 2: Regression and Regularization Algorithms (Session 2)
- Read more about Intro to Data Science Module 2: Regression and Regularization Algorithms (Session 2)
This webinar is part of the Introduction to Data Science Webinar Series
Practicum session
- Regression with many correlated variables
- Automatic variable selection, early approaches and problems
- Gradient descent
- Regularization (L1 vs L2 vs ElasticNet)
Watch recorded presentation below.
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