PHDA testimonials

"I think the highly trained, expert instructors, who are approachable and provide excellent feedback are a major strength. Certainly, the small class sizes allow for that individualized feedback and a continual dialogue with instructors in the discussion boards as well. I think the Moodle course site is very easy to use, and in general, the courses were very well organized.

Through the program, I came to learn about the enormous benefits of using already-collected administrative health data, and the process for doing so. I think my skills with SAS and ArcGIS will be the most useful to me, but the larger concepts of data integrity, (PHDA 01), what type of analysis is used in certain situations (PHDA 02), and considerations when doing spatial analysis (PHDA 04) are immensely important as well."

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"This course is a rich and nuanced exploration of the interplay of spatial associations between population health and exposure to multiple risk factors including pathogen/toxin load, socio-economic influences and ‘built environment’ indices. Well-paced and supported ‘labs’ move from mapping skills and associated ethics to the use of spatial statistics in assessing distribution in population health. Required readings are current and wide-ranging. The final project provides a wonderful opportunity to use acquired tools to explore in depth a personal research project of interest.

I highly recommend this course to all in population health – from analysts and city planners to physicians and veterinarians. The power of GIS to put “place” into the population health equation is a powerful tool. This course illuminates this exceptionally."

"The course was well structured. Through the course, I learned: a formal approach to developing, structuring and answering a research question using administrative data; how to clearly define the study population; how to develop a flow chart to illustrate, and guide, the development of an analytical dataset; and SAS skills. It was an excellent course and a good refresher for me, as it has been 5 years since I’ve done data analysis. A number of course assignments required me to pair with a fellow student and undergo peer review of one another’s code. It was interesting to see how my peer structured their code to produce their dataset. I enjoyed and learned a lot from this exchange. The flexibility of distance learning meant that it was possible to complete work at my own pace and schedule. It was also helpful to learn from students in different locations, who worked in related fields but had different strengths, skills and perspectives.

Finally, this course gave me experience working with administrative datasets and tools that can be difficult to access on the job. The opportunity to work with samples of real data exposed me to some of the challenges I could face at work in learning, cleaning, formatting and analyzing the data. I would highly recommend this course and the PHDA program to my colleagues. It’s a well-structured and organized program and provides a good theoretical and practical approach to population health data analysis."

"It was really good for me to gain more experience working with large data sets and use a secure research environment to get used to the complexities of various kinds of datasets we come across daily. The spatial data and network analysis work, applied longitudinal analysis techniques and foundational SAS skills I learned really benefitted my research work. Now when I am faced with new problems, I feel better equipped and much more confident to handle them.

I have done some online courses before. I felt the courses in this program were well organized. The labs were very useful and very applied, instructors were always available to answer questions and the weekly or biweekly course modules provided structure but also some flexibility."

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"I developed an understanding of not only how descriptive statistics can be geographically interpreted, but also how inferential statistics can be geographically interpreted. It taught me how to begin thinking spatially in-terms of patterns of health outcomes – neighbourhood make up, neighbourhood location, infrastructure, environmental concerns, etc."

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