phd health data science canada

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7 Best Universities in Canada to Study Ph.D. in Data Science

A Ph.D. degree is the highest academic qualification anyone can earn. And with Harvard Business Review declaring data scientist to be the sexiest job of the 21 st century, a Ph.D. in Data Science should probably be called the sexiest university degree of the century.

Universities in Canada, like the ones in other highly advanced countries, are offering such degrees in abundance. Here, we have listed the seven best Canadian universities that offer a Ph.D. degree in Data Science.

Top Schools Offering Ph.D. in Data Science Programs in Canada

1. university of british columbia.

• Tuition Fees | Scholarships

Beginning its journey over a hundred years ago, the University of British Columbia (UBC) offers higher education to nearly 65,000 students, including over 17,000 international students.

It has two main campuses sited in Vancouver and Okanagan of the province of British Columbia. It is recognized as one of the top 40 universities in the world and top three universities in Canada by a number of major university rankings.

Nobel laureates like economist Robert Mundell and physicist Bertram Brockhouse are a couple of notable alumni.

UBC’s Department of Computer Science administers a Ph.D. in Computer Science program. Its research areas include a number of concepts relevant to Data Science like Data Integration, Text Mining, Web Databases, and Optimization.

It is an on-campus program that is only available in a full-time format. It takes place at the Vancouver campus and can be completed within three to five years.

2. HEC Montréal

HEC Montréal is actually not a university; rather, it is a graduate school of the Université de Montréal. Founded in 1907, the business school currently has over 14,000 students.

All of its classes take place in its two buildings in Montreal, Quebec: the Côte-Sainte-Catherine building and the Decelles building.

Despite being a French-language institution, it also offers programs that have English as the medium of instruction.

Louis R. Chênevert, the former CEO of United Technologies Corporation and president of Pratt & Whitney, graduated from this institution.

HEC Montréal offers a Ph.D. in Administration – Data Science program in collaboration with three other universities: McGill University , Concordia University , and Université du Québec à Montréal (UQAM). The partnership provides students with access to resources from all four Montreal-based institutions.

The English-language program requires 90 credits to be completed in full-time study mode. The duration of the program is typically four to five years.

3. University of Waterloo

Next on our list of universities in Canada with a Ph.D. in Data Science is the University of Waterloo . Founded in 1957, the University of Waterloo (UWaterloo) has a student population north of 40,000.

Around 20 percent of all undergraduate students and 40 percent of all graduate students have come from outside Canada. According to U.S. News and World Report Best Global Universities 2019, the university located in Waterloo is Canada’s best for Computer Science and second-best for Engineering.

It is the alma mater of Rasmus Lerdorf, the programmer who authored the first two versions of the PHP scripting language.

UWaterloo’s Ph.D. in Computer Science includes research areas like Databases, Information Retrieval, and Machine Learning that are relevant to the field of Data Science.

Students may take on this on-campus program in either full-time or part-time study mode. Admission takes place thrice a year at the start of the academic terms Fall, Winter, and Spring.

4. University of Alberta

The University of Alberta is a top-tier Canadian research university based primarily in Edmonton, the capital of Alberta. It was established in 1908, the same year that UBC came into being. The total student enrolment stands at over 40,000.

Nobel-winning physicist Richard E. Taylor and former Canadian prime minister Joe Clark graduated from this university.

Alberta’s Department of Computing Science runs a Ph.D. in Computing Science that has a wide range of research areas. A few of them, including Database Systems and Machine Learning, are related to the field of Data Science.

There’s also a Ph.D. in Statistical Machine Learning offered by the department in collaboration with the Department of Mathematical and Statistical Sciences. These are full-time programs that are ideally completed within five years.

5. Dalhousie University

Founded over two hundred years ago, Dalhousie University (Dal) is one of the oldest universities in the Great White North. Its student population is around 19,000, which includes nearly 4,000 international students representing over 115 nationalities.

Each of its three campuses is located in Halifax, the capital of Nova Scotia. The university is the alma mater of Nobel-winning astrophysicist Arthur B. McDonald, Canadian PMs R.B. Bennett, and Brian Mulroney, as well as former Xerox Corporation CEO and chairman Charles Peter McColough.

Dal offers a Ph.D. in Computer Science which allows students to specialize and develop deep expertise in Data Analytics. The Faculty of Computer Science’s Big Data Analytics & Machine Learning research cluster, which is based in the Institute for Big Data Analytics, helps students to conduct independent and original research. The Ph.D. program takes around three to four years to complete.

6. University of New Brunswick

The University of New Brunswick (UNB) started off in 1785 as Canada’s first English-language university. It has two campuses: one situated in New Brunswick’s capital Fredericton, and the other in the port city of Saint John in the same province. The university has more than 10,000 students hailing from over 100 countries. It was recognized as the country’s most entrepreneurial university in 2014 by Startup Canada.

UNB’s Faculty of Computer Science offers a Ph.D. in Computer Science one of the many research areas is Data Management, Analytics, and Mining. The program can be completed in three years. While it is ideally a full-time program, students could be permitted to take on the program on a part-time basis under certain conditions.

7. Ryerson University

Beginning its journey in 1948 as a postsecondary institute with merely 250 students, Ryerson University has grown into a full-fledged university with over 45,000 students.

It is located in Toronto, Ontario, and is the province’s most applied-to university relative to available accommodation.

Four Seasons Hotels and Resorts founder and chairman Isadore Sharp and former Maple Leaf Sports & Entertainment president and COO Tom Anselmi are a couple of its notable alumni.

Ryerson’s Faculty of Science administers a Ph.D. in Computer Science which provides students with the option of specializing in Data Science and related subjects like Machine Learning and Data Mining. The nominal duration of the thesis-based program is three years. The format for this program is full-time.

FAQS About Studying Ph.D. in Data Science

Which university in canada is best for a ph.d. in data science.

When it comes to Canada, the University of British Columbia would probably be your best pick for getting a Ph.D. in data science. Requiring nearly 5-6 years of your time, this professional degree program will challenge its students to analyze data from a wide range of domains while allowing each student to solve real-world problems created by partnered businesses.

How Long Does It Take to Earn a Ph.D. in Data Science?

If you want to graduate with a Ph.D. in data science, the time required ranges from 4 years to 9 years, depending on the person. To go down this track, you would probably apply for a Doctor of Philosophy in Computer Science and take the track of data analysis, which is a major part of data science.

If you just want to complete a Master’s degree in data science, you only need to spend 10 months of your time in this accelerated professional degree program. Analyzing data from a real-world environment, the experience that you would get from these types of programs would be second to none!

We hope that this article on universities in Canada with Ph.D. in Data Science was helpful. Make sure to also check out our  Data Science Programs for International Students  for some of the currently open data science courses around the world!

About the Author: Hyun Lee

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Graduate studies in Health Information Science

Western’s Faculty of Information and Media Studies (FIMS) and Faculty of Health Sciences (FHS) offer three joint degrees: a PhD in Health Information Science, a one year, course-based Master of Health Information Science and a two year, thesis-based Master of Health Information Science. The PhD program is a research-intensive program designed for students who want to do independent, original research in health information science. The master's options are geared to either continued academic study, or entry into the job field.

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Gain skills & knowledge in a rapidly expanding area of research

All three degree options provide students with fundamental knowledge in health and health care, including:

• Public health
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Eliasu Yakubu

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Health Informatics (PhD)

Our PhD in Health Informatics will prepare scholars to discover and extend their scientific knowledge and advance the science and practice of health informatics.

The program is built around our core interdisciplinary specializations:

• design and structure of health information systems
• implementation and evaluation of health information systems
• health information systems applications
• health data science and analytics
• patient and equity-focused health technology interventions

Expected length	Project or thesis	Course-based
7 years	Yes	No

Quick facts

Students in this program will:

• generate new knowledge through research and testing of theory in health informatics
• contribute to solutions that advance health informatics and health care in a culturally diverse society
• translate health informatics research findings into practice and policy across health care systems
• represent a health informatics perspective in research, practice, education and scholarly endeavours
• demonstrate leadership and management competencies in health informatics
• develop system evaluation and digital transformation plans

Find a supervisor

You may list a potential supervisor on your application, but this is not required.

Abdul Roudsari

Professor, Graduate Advisor Modelling and Simulation in healthcare; modelling methodology for health resource management; clinical decision support, machine learning and artificial intelligence – development and evaluation of decision support systems; evaluation methodologies with particular application in telemedicine. More recently my interests have developed into : Telemedicine technology, temporal representation and reasoning; utilisation of business intelligence in healthcare; shared decision-making, personalised health records and environmental sensing for health. Application area Chronic diseases.

[email protected]

Professor Data interoperability; Health database & data warehousing; AI & Data Mining application in healthcare, and e-health.

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Andre Kushniruk

Director, Professor Usability of Health Information Systems; Human Factors in Healthcare; Clinical Informatics; Consumer informatics; Decision Support Systems; Healthcare Decision Making; Cognitive Informatics; AI in Healthcare; Evaluation methods; Healthcare System Design; Data Analytics and Visualization

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Claudia Lai

Assistant Professor Digital Innovations that Promote Healthy Aging at home, Shared Decision Making, and Public Health; Community-Based Participatory Research; Digital Health Equity; Integrated Care; Learning Health Systems, Patient Portals

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Assistant Professor

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Dillon Chrimes

Assistant Teaching Professor Dr. Dillon Chrimes is currently not available to supervise graduate students.

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Elizabeth Borycki

Professor Human Factors (Safety, Workflow and Usability); Design (User Interfaces, Heuristics and Guideline Development); Implementation Science (Technology Strategy, Implementation and Evaluation); Knowledge Management (Information Needs; Information Seeking; Decision Support Systems); Virtual Care (Mobile Health, Sensors, Medical Devices, Smart Homes, Telehealth); Data Science (Analytics, Dashboard Visualizations and AI); Clinical Informatics (ePrescribing, eMedRec, eMedication Administration Systems, Electronic Health Records); Nursing Informatics; Health Informatics (Professional Competency Development, Curriculum Design)

[email protected]

Helen Monkman

Assistant Professor, Undergraduate Advisor Human Factors; User Experience; Usability; Consumer Health Informatics; eHealth Literacy or Digital Health Literacy; Information Visualization.

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Karen L. Courtney

Professor Areas of interest: Telehealth; m-Health; Information Technology Ethics; Community-based Informatics; Gerontechnology; Nursing Informatics; Health Terminologies and Standards Modernization

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Simon Minshall

Assistant Teaching Professor, Health Terminology and Interoperability Standards Certificate Coordinator Simon Minshall is currently not available to supervise graduate students.

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Your program details

Application deadlines.

September 2025 entry – apply by January 15, 2025

Admission requirements

Program specific requirements.

• If you have a master’s degree in a field other than health informatics, you should apply to our MSc in Health Informatics program  and indicate in your letter of intent that you are interested in applying to the PhD program in the future.  Admission to the MSc program does not guarantee you future admission to the PhD program.
• Successful completion of a graduate level statistics course is required before you apply

As part of your application, you must submit:

• Three assessment reports. At least two should be from academics who can assess your capacity to conduct independent scholarly work and research.
• A curriculum vitae   that summarizes your education background, employment history, professional/academic affiliations, and other achievements such as publications or awards.
• One or two sample publications or conference proceedings (if available). For each publication or proceeding, please include the full citation, indicate your percentage of contribution, your role and an electronic copy (preferably PDF).
• Thesis or research project (if available). Please provide an electronic copy (preferably PDF).
• Letter of intent summarizing why you are interested in earning the PhD in Health Informatics. Include your research interests and expectations of program in terms of personal and professional learning. 
• Indicate if you have made contact with a faculty member regarding a possible supervisory arrangement. Having a prospective supervisor is not required.
• Successful completion of a graduate level statistics course is required before application.
• GRE scores must be submitted as part of the application.

Completion requirements

View the minimum course requirements for this program.

Funding & aid

Note : Co-op or assistantship is based on availability and not guaranteed.

All students are reviewed for graduate student funding annually. Funding is based on academic performance. We will consider students with a GPA of A- (7.0) or higher and adequate academic progress in the first year.

There is a maximum of two years of graduate student funding available from the school. Students on leave are not eligible for this funding.

You are encouraged to seek additional research funding opportunities through grants and additional financial assistance through university level awards, teaching assistantships and research assistantships.

• PhD students meeting the eligibility requirements will receive an entrance award.
• PhD students meeting the eligibility requirements and making adequate academic progress in the first year will be eligible for a second year of funding.
• Students who began in the MSc program and have successfully applied to PhD program will be considered for PhD first year funds after their successful admission to the PhD program. Example: A student applying in December 2019 and receiving admission to the PhD program would receive first year PhD funding beginning in September 2020. Eligibility criteria for MSc to PhD students is the same as PhD students.

Tuition & fees

Estimated minimum program cost*

* Based on an average program length. For a per term fee breakdown view the tuition fee estimator .

Estimated values determined by the tuition fee estimator shall not be binding to the University of Victoria.

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Biostatistics (PhD)

Program description.

The Doctor of Philosophy (Ph.D.) in Biostatistics offered by the Department of Epidemiology, Biostatistics, and Occupational Health in the Faculty of Medicine & Health Sciences is a research-intensive program that emphasizes engaging and cutting-edge learning opportunities. The program's objective is to equip students with skills in independent thinking, data analysis, and scientific communication to pursue professional opportunities in academia or industry.

Keywords: causal inference, dynamic treatment regimes, longitudinal data, spatial statistics, statistical genetics, time series, statistics, data science, Bayesian, clinical trials, causal inference, disease mapping, genomics, pharmacoepidemiology, spatio-temporal processes, statistical genetics, statistical computing

Unique Program Features

• The program trains students to become independent scientists able to develop and apply statistical methods in medicine and biology and make original contributions to the theoretical and scientific foundations of statistics in these disciplines;
• Faculty members focus their research in areas that include survival analysis, non-parametric and semi-parametric modelling, analysis of longitudinal data, causal inference, statistical computing, classification and regression trees, methods for evaluating diagnostic accuracy, Bayesian statistics in medicine, statistical methods for clinical trials, and the design and analysis of epidemiologic studies;
• Graduates pursue careers as biostatisticians in government agencies (e.g., the Public Health Agency of Canada, Statistics Canada, NRC, Santé Québec, INSPQ, regional departments of public health, health technology assessment units), the pharmaceutical industry and the contract research organizations (CROs) that perform statistical work for industry, in academia (e.g., Departments of Biostatistics, Epidemiology, and Statistics) as well as hospital and other medical research institutes.

University-Level Admission Requirements

• An eligible Bachelor's degree with a minimum 3.0 GPA out of a possible 4.0 GPA
• English-language proficiency

Each program has specific admission requirements including required application documents. Please visit the program website for more details.

Visit our Educational credentials and grade equivalencies and English language proficiency webpages for additional information.

Program Website

PhD in Biostatistics website

Department Contact

Graduate Program graduate.eboh [at] mcgill.ca (subject: PhD%20in%20Biostatistics%20website) (email)

Available Intakes

Application deadlines.

Intake	Applications Open	Application deadline - International	Application Deadline - Domestic (Canadian, Permanent Resident of Canada)
FALL	September 15	December 1	December 1
WINTER	N/A	N/A	N/A
SUMMER	N/A	N/A	N/A

Note : Application deadlines are subject to change without notice. Please check the application portal for the most up-to-date information.

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• Application Steps webpage
• Submit Your Application webpage
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• Graduate Funding webpage

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Consult our full list of our virtual application-focused workshops on the Events webpage.

Department and University Information

Graduate and postdoctoral studies.

PhD Public Health Sciences

 OUR PhD PROGRAM 2023

The Department of Public Health Sciences has broadened its PhD program to include all areas of research expertise represented in our faculty including, but not limited to, epidemiology, biostatistics, qualitative, mixed- and community-based methods, the use of health and public-health services, program evaluation, clinical epidemiology, health equity, global health, indigenous health, and health economics.

Our programs place an emphasis on close faculty-student relations and a philosophy that puts the student first. Through coursework, thesis opportunities, and involvement in the academic life of our department, our students graduate with an in-depth understanding of public health research. Our graduates are able to function as independent investigators in academic, health-research institutes and health-research government agencies, or as emerging public-health leaders in government or the private sector.

In the coming year, our PhD program in the Department of Public Health Sciences at Queen’s University has a number of exciting opportunities for qualified applicants. The following members of our department have each expressed an interest in supervising a new PhD student:  

Dr. Susan Bartels is a Clinician-Scientist in the Department of Emergency Medicine with a cross appointment to Public Health Sciences. Her research focuses on the health and well-being of women and children affected by humanitarian crises around the globe. Dr. Bartels is interested in the social determinants of health and uses innovative research methods to provide evidence intended to inform policy and programming that will improve health outcomes and mitigate the risks of natural disasters, armed conflict and forced displacement.  

Dr. Susan Brogly is an epidemiologist with research interests in the area of perinatal epidemiology, surgical outcomes, and advanced epidemiologic methods. Dr. Brogly used both population-based administrative health care data (ICES, Medicaid) and primary data collection in her studies.  

Dr. Steven Brooks is a Clinician-Scientist and Emergency Physician in the Department of Emergency Medicine who conducts research in the areas of cardiac arrest and resuscitation. Dr. Brooks may have availability for a PhD student willing to work with the Canadian COVID-19 Emergency Department Rapid Response Network. This is a developing registry funded by CIHR and the Ontario government, tracking patients with suspected and confirmed COVID-19 who attend one of 50 EDs in the country.

Dr. Bingshu Chen is a biostatistician with an interest in survival analysis and generalized linear models. He has developed biomarker threshold models to predict treatment benefit in cancer clinical trials. His other research interests include analysis of health economic data, statistics computing and missing data problems.  

Dr. Anne Duffy is a Clinician-Scientist. She has longitudinal data spanning two decades in high-risk offspring of bipolar parents and has up to two years of psychosocial, clinical and familial data from a representative cohort of undergraduate university students to understand mental health and academic outcomes. These databases provide several opportunities that would make for an interesting thesis including using joint modelling, multi-state and survival analysis. Further information on Dr. Duffy’s research can be found at: https://www.mdco.ca/research/ .

Dr. Jennifer Flemming is a Clinician-Scientist who studies the link between cirrhosis and biliary tract cancer and the burden of chronic liver disease and cirrhosis in Ontario. She uses large population-based databases housed at ICES. Her goal is to improve management strategies for Canadians with liver disease.

Dr. Ana Johnson is a health economist who conducts economic evaluations of health care programs, cost-effectiveness analyses, assessments of resource allocations and use of health technologies.

Dr. Will King is a molecular epidemiologist whose research program seeks to identify modifiable risk factors for cancer. Dr. King studies intermediate markers of cancer risk and genetic susceptibility to better understand environment-cancer relationships.

Dr. Diane Lougheed is a Clinician-Scientist with a research interest in asthma and the development of better information technologies to improve the care of patients with asthma. Dr. Lougheed conducts health services and outcomes research and guideline implementation research in asthma and often uses the ICES data holdings to conduct her work.

Dr. Zihang Lu is a Biostatistician. His research focuses on developing and applying statistical and machine learning methods to answer clinical and epidemiological research questions. His current research interests are in longitudinal data, survival data and high-dimensional data modeling. He is also interested in Bayesian statistics, causal inference and data fusion.

Dr. Maria Ospina is an associate professor with the Department of Public Health Sciences at Queen’s University, and a clinical epidemiologist, and population-health researcher in the areas of perinatal and early childhood health. Her research program (DMETRE) uses a life-course approach and a variety of epidemiological methods (observational studies, systematic reviews, GIS analysis, mixed-methods designs), to assess the developmental origins of health inequalities, and how critical periods of human development such as pregnancy and the first 1,000 days of life influence future health.

Dr. Paul Peng is a is a biostatistician with research interests in survival analysis with a focus on cure models, longitudinal and panel data modeling, statistical computation methods for big data, biostatistical methods for epidemiological and clinical trial research.

Dr. William Pickett is in the Faculty of Applied Health Sciences at Brock University and is an Adjunct Professor in the Department of Public Health Sciences at Queen’s University, and an Adjunct Professor in the College of Medicine at the University of Saskatchewan. He is a trained epidemiologist whose research interests include: injury and violence prevention; injury and illness in rural and farm populations; and health and its social determinants in adolescent populations, with a primary focus on pediatric violence and injury. Using public health surveillance, analytical and experimental epidemiology, and mixed methods approaches, this work has provided critical insight for policy/health promotion initiatives in Canada, the US and Europe.

Dr. Amrita Roy is a family physician and MD-PhD clinician-scientist in the Departments of Family Medicine and Public Health Sciences at Queen’s. A settler ally with a research focus in Indigenous health, Dr. Roy works in close collaboration with Indigenous peoples in community-engaged research centred on the principles of Ownership, Control, Access, and Possession (OCAP). Apart from Indigenous health, Dr. Roy’s other areas of research interest include immigrant and refugee health, women’s health, youth health, and global health. Methodologically, Dr. Roy has expertise in quantitative, qualitative, mixed- and multiple-methods approaches to health research, in addition to community-based and participatory research approaches.  Fall 2023 PhD opportunity with Dr. Roy:  Opportunity for a PhD student starting fall 2023 in a CIHR-funded Indigenous health research project on sleep and mental health, in partnership with Akwesasne Mohawk Nation .

Dr. Sahar Saeed is an epidemiologist and health-services researcher. Dr. Saeed primarily investigates retention and access to health care among populations including persons living with HIV/AIDS, hepatitis C and chronic liver disease. She uses primary data collection, population-based administrative health-care data and novel surveillance tools (GPS) to answer her research questions. For more information on her research interest, visit her website at Epidemiologist | Sahar Saeed .

Dr. Bradley Stoner is Professor and Head, Department of Public Health Sciences and Professor of Medicine at Queen’s University. An infectious disease physician and medical anthropologist, Dr. Stoner’s research focuses on the epidemiology, clinical care, control and prevention of sexually transmitted infections (STI) including HIV. 

Dr. Wei Tu  is a biostatistician with research interests in data science and its application in health care. His research focuses on translating different sources of high-dimensional data into informed clinical decision-making. The topics he is working on include personalized medicine, data privacy and causal inference.  

Dr. Maria Velez is a Clinician-Scientist with research interests in reproductive and perinatal epidemiology. Her current research program focuses on infertility and pregnancy outcomes, and the reproductive health of young women with cancer. She uses population-based cohort studies including databases housed at ICES.  

Dr. Paul Villeneuve is an environmental and occupational epidemiologist. His research program is focused on quantifying the health effects from exposure to outdoor air pollution, noise, low levels of radiation, as well as the benefits of urban greenness and walkability.  In addition to carrying out spatiotemporal exposure studies in Canada and Grenada (West Indies), he also uses large population-based databases housed in Statistics Canada’s Research Data Centers, and ICES.

If you have a demonstrable interest in the work of one or more of these professors we encourage you to reach out to them to discuss the possibility of supervision.

Our PhD students are guaranteed minimum funding of $21K per year for four years with further income possibilities coming from Teaching Assistantships, Research Assistantships, or Research Fellowships. Many of our students receive national or provincial scholarships.

For further information about our PhD Program, you can contact the Reserach Program Director, Dr. Ian Janssen at [email protected]    or the Graduate Assistant at [email protected] .  Note that all applicants must meet the entry requirements to the program:  https://phs.queensu.ca/programs-courses/degree-programs/phd-public-health-sciences/how-apply  

Student Stories

Program Contacts

• Research Program Director  Dr. Ian Janssen [email protected]
• Graduate Assistant   [email protected]

Affiliated Research Groups

• Canadian Cancer Trials Group
• Centre for Health Services and Policy Research 
• Emergency Medicine & Injury Research Group
• Cancer Care & Epidemiology, Queen's Cancer Research Institute
• Queen's - ICES Health Services Research 
• Centre for Studies in Primary Care
• Centre for Obesity and Research Education 
• Practice and Research on Nursing (PRN) Group
• KFL &A Public Health

• Graduate School
• Prospective Students
• Graduate Degree Programs

Doctor of Philosophy in Population and Public Health (PhD)

Go to programs search

The School of Population and Public Health offers a research-oriented PhD program that enables students with a masters degree to advance their knowledge and skills in epidemiological and biostatistical methods. Students will further their research training by applying these methods to independent thesis research under the supervision of a faculty member. Students can pursue thesis research in a wide variety of topics related to the health of populations and the delivery of health services.

For specific program requirements, please refer to the departmental program website

While I explored other schools across Canada, UBC was my ultimate choice. I call Vancouver home, and I found supervisors at UBC whose research aligns with my interests and could support me through my graduate studies. Relationships are very important to me, and I wanted to build on my existing networks and connections for my PhD research.

Jeffrey Morgan

Quick Facts

Program Enquiries

Admission information & requirements, 1) check eligibility, minimum academic requirements.

The Faculty of Graduate and Postdoctoral Studies establishes the minimum admission requirements common to all applicants, usually a minimum overall average in the B+ range (76% at UBC). The graduate program that you are applying to may have additional requirements. Please review the specific requirements for applicants with credentials from institutions in:

• Canada or the United States
• International countries other than the United States

Each program may set higher academic minimum requirements. Please review the program website carefully to understand the program requirements. Meeting the minimum requirements does not guarantee admission as it is a competitive process.

English Language Test

Applicants from a university outside Canada in which English is not the primary language of instruction must provide results of an English language proficiency examination as part of their application. Tests must have been taken within the last 24 months at the time of submission of your application.

Minimum requirements for the two most common English language proficiency tests to apply to this program are listed below:

TOEFL: Test of English as a Foreign Language - internet-based

Overall score requirement : 100

IELTS: International English Language Testing System

Overall score requirement : 7.0

Other Test Scores

Some programs require additional test scores such as the Graduate Record Examination (GRE) or the Graduate Management Test (GMAT). The requirements for this program are:

The GRE is required by some applicants. Please check the program website.

2) Meet Deadlines

3) prepare application, transcripts.

All applicants have to submit transcripts from all past post-secondary study. Document submission requirements depend on whether your institution of study is within Canada or outside of Canada.

Letters of Reference

A minimum of three references are required for application to graduate programs at UBC. References should be requested from individuals who are prepared to provide a report on your academic ability and qualifications.

Statement of Interest

Many programs require a statement of interest , sometimes called a "statement of intent", "description of research interests" or something similar.

• Supervision

Students in research-based programs usually require a faculty member to function as their thesis supervisor. Please follow the instructions provided by each program whether applicants should contact faculty members.

Instructions regarding thesis supervisor contact for Doctor of Philosophy in Population and Public Health (PhD)

Citizenship verification.

Permanent Residents of Canada must provide a clear photocopy of both sides of the Permanent Resident card.

4) Apply Online

All applicants must complete an online application form and pay the application fee to be considered for admission to UBC.

Tuition & Financial Support

Fees	Canadian Citizen / Permanent Resident / Refugee / Diplomat	International
	$114.00	$168.25
Tuition *
Installments per year	3	3
Tuition	$1,838.57	$3,230.06
Tuition (plus annual increase, usually 2%-5%)	$5,515.71	$9,690.18
Int. Tuition Award (ITA) per year ( )		$3,200.00 (-)
Other Fees and Costs
(yearly)	$1,116.60 (approx.)
	Estimate your with our interactive tool in order to start developing a financial plan for your graduate studies.

Financial Support

Applicants to UBC have access to a variety of funding options, including merit-based (i.e. based on your academic performance) and need-based (i.e. based on your financial situation) opportunities.

Program Funding Packages

From September 2024 all full-time students in UBC-Vancouver PhD programs will be provided with a funding package of at least $24,000 for each of the first four years of their PhD. The funding package may consist of any combination of internal or external awards, teaching-related work, research assistantships, and graduate academic assistantships. Please note that many graduate programs provide funding packages that are substantially greater than $24,000 per year. Please check with your prospective graduate program for specific details of the funding provided to its PhD students.

Average Funding

• 24 students received Teaching Assistantships. Average TA funding based on 24 students was $6,707.
• 38 students received Research Assistantships. Average RA funding based on 38 students was $18,770.
• 17 students received Academic Assistantships. Average AA funding based on 17 students was $5,352.
• 57 students received internal awards. Average internal award funding based on 57 students was $10,782.
• 22 students received external awards. Average external award funding based on 22 students was $28,705.

Scholarships & awards (merit-based funding)

All applicants are encouraged to review the awards listing to identify potential opportunities to fund their graduate education. The database lists merit-based scholarships and awards and allows for filtering by various criteria, such as domestic vs. international or degree level.

Graduate Research Assistantships (GRA)

Many professors are able to provide Research Assistantships (GRA) from their research grants to support full-time graduate students studying under their supervision. The duties constitute part of the student's graduate degree requirements. A Graduate Research Assistantship is considered a form of fellowship for a period of graduate study and is therefore not covered by a collective agreement. Stipends vary widely, and are dependent on the field of study and the type of research grant from which the assistantship is being funded.

Graduate Teaching Assistantships (GTA)

Graduate programs may have Teaching Assistantships available for registered full-time graduate students. Full teaching assistantships involve 12 hours work per week in preparation, lecturing, or laboratory instruction although many graduate programs offer partial TA appointments at less than 12 hours per week. Teaching assistantship rates are set by collective bargaining between the University and the Teaching Assistants' Union .

Graduate Academic Assistantships (GAA)

Academic Assistantships are employment opportunities to perform work that is relevant to the university or to an individual faculty member, but not to support the student’s graduate research and thesis. Wages are considered regular earnings and when paid monthly, include vacation pay.

Financial aid (need-based funding)

Canadian and US applicants may qualify for governmental loans to finance their studies. Please review eligibility and types of loans .

All students may be able to access private sector or bank loans.

Foreign government scholarships

Many foreign governments provide support to their citizens in pursuing education abroad. International applicants should check the various governmental resources in their home country, such as the Department of Education, for available scholarships.

Working while studying

The possibility to pursue work to supplement income may depend on the demands the program has on students. It should be carefully weighed if work leads to prolonged program durations or whether work placements can be meaningfully embedded into a program.

International students enrolled as full-time students with a valid study permit can work on campus for unlimited hours and work off-campus for no more than 20 hours a week.

A good starting point to explore student jobs is the UBC Work Learn program or a Co-Op placement .

Tax credits and RRSP withdrawals

Students with taxable income in Canada may be able to claim federal or provincial tax credits.

Canadian residents with RRSP accounts may be able to use the Lifelong Learning Plan (LLP) which allows students to withdraw amounts from their registered retirement savings plan (RRSPs) to finance full-time training or education for themselves or their partner.

Please review Filing taxes in Canada on the student services website for more information.

Cost Estimator

Applicants have access to the cost estimator to develop a financial plan that takes into account various income sources and expenses.

Career Outcomes

60 students graduated between 2005 and 2013: 1 is in a non-salaried situation; for 3 we have no data (based on research conducted between Feb-May 2016). For the remaining 56 graduates:

Sample Employers in Higher Education

Sample employers outside higher education, sample job titles outside higher education, phd career outcome survey, alumni on success.

Lianping Ti

Job Title Research Scientist

Employer BC Centre for Excellence in HIV/AIDS

Enrolment, Duration & Other Stats

These statistics show data for the Doctor of Philosophy in Population and Public Health (PhD). Data are separated for each degree program combination. You may view data for other degree options in the respective program profile.

ENROLMENT DATA

	2023	2022	2021	2020	2019
Applications	37	41	52	40	42
Offers	16	21	22	22	19
New Registrations	12	13	16	16	11
Total Enrolment	91	91	91	84	76

Completion Rates & Times

Upcoming doctoral exams, friday, 13 september 2024 - 12:30pm - 202, school of population and public health, 2206 east mall, wednesday, 25 september 2024 - 9:00am - room 200.

• Research Supervisors

Advice and insights from UBC Faculty on reaching out to supervisors

These videos contain some general advice from faculty across UBC on finding and reaching out to a supervisor. They are not program specific.

This list shows faculty members with full supervisory privileges who are affiliated with this program. It is not a comprehensive list of all potential supervisors as faculty from other programs or faculty members without full supervisory privileges can request approvals to supervise graduate students in this program.

• Amri, Michelle (Global Health Ethics; normative nature of health equity; Health Equity; Public Policy; Governance; Global Health; International health)
• Anis, Aslam (cost effectiveness of AIDS treatments; drug assessments – pharmacoeconomics; health care economics; health regulations, Health economics, rhematoid arthritis, biologic therapies)
• Bansback, Nick (inform policies and practices in health through the application of)
• Bhatti, Parveen
• Black, Charlyn (Public and population health)
• Brauer, Michael (Environmental and occupational health and safety; Health sciences; Public and population health; air pollution; built environment; Community Health / Public Health; environmental health; environmental epidemiology; healthy cities; remote sensing)
• Brussoni, Mariana (Developmental psychology; Psychosocial, sociocultural and behavioral determinants of health; Population health interventions; injury prevention; Children's outdoor play; Risky play; Parenting; health behaviour change; Implementation Science)
• Bryan, Stirling (Economics of health care, policy, from UK)
• Cox, Susan (Other medical sciences; Sociology and related studies; Arts (arts, history of arts, performing arts, music), architecture and design)
• Davies, Hugh William (Environmental and occupational health and safety; Health sciences; Public and population health; Antineoplastic drug hazards; Community Health / Public Health; environmental health; Exposure Assessment; Noise and Health; Occupational Health; Occupational Safety and Health)
• Deering, Kathleen (Medical, health and life sciences)
• Dummer, Trevor (health geography, cancer prevention, environmental exposures, health inequalities, geographic information science, obesity, risk factors, Environmental epidemiology and environment and health interactions, with specific emphasis on cancer etiology and cancer prevention)
• Elango, Rajavel (Protein Nutrition, Maternal-Fetal Nutrition, Childhood Malnutrition, Amino Acid Metabolism, Human Nutrition )
• Frank, Erica (Health sciences; Public and population health; Other education; Free accredited education; Preventive Medicine; Sustainable Architecture and Landscape Architecture; Holocaust studies; Exile Reintegration)
• Gadermann, Anne (Social determinants of health; Housing and homelessness; Quality of)
• Gilbert, Mark (Public and population health; Development, implementation, evaluation and scale-up of innovative sexual health programs; Gay men’s sexual health, including sexual health literacy; Synergistic and integrated dynamics of infectious diseases, mental illness and other conditions)
• Greyson, Devon (Health-related information practices of youth, parents, and families; Intersection between information practices and health behavior,; Cannabis use decision making in pregnancy and lactation; Vaccine confidence and decision making about vaccination; Disinformation in social media support communities; Online communication among young parents)
• Guhn, Martin (Developmental psychology; Psychosocial, sociocultural and behavioral determinants of health; social context and child development/well-being; Population health; social determinants of health)
• Henderson, Sarah (Environmental and occupational health and safety; wildfire smoke; air pollution; Extreme weather events; environmental health; radon gas; Food safety; Water quality)
• Janssen, Patricia (Health sciences; Public and population health; Gestation / Parturition; health of marginalized women; Lifestyle Determinants and Health; maternal child health; mobile health for pregnancy and parenting; Perinatal Period; social determinants of health)
• Joseph, K.S. (Pregnancy complications, preterm birth, fetal growth, infant mortality, neonatal)
• Kalua, Khumbo (Population health interventions; Infectious diseases; Global health; Epidemiology (except nutritional and veterinary epidemiology); Neglected Tropical Eye Diseases; Global Eye Health; Cluster Randomized Trials; Implementation Science; International Global Health; Community Based Research; Clinical trials)
• Karim, Ehsan (Biostatistical methods; Survey methodology and analysis; Statistical learning; Epidemiology (except nutritional and veterinary epidemiology); Public and population health, n.e.c.; Causal inference; Biostatistics; Statistics; Machine Learning; data science; Survey data analysis; multiple sclerosis)
• Kassam, Rosemin (Medical, health and life sciences; Child Health, Malnutrition, Adult Chronic Disease, Geriatrics)
• Kazanjian, Arminee (Cancer Survivorship, Knowledge Exchange and, Translation, Psychosocial oncology, Palliative care in cross-cultural context, Vulnerable populations, including women)

Doctoral Citations

Year	Citation
2024	Dr. Gill examined how different types of childhood poverty experience affect children's development, health, and school success from kindergarten to high school graduation in British Columbia, and how these relationships differ by the child's immigration background. This work can inform intervention and policy to reduce harms related to poverty.
2024	Should patients with coronary artery disease consider stenting if they must wait for bypass surgery? Dr. Hardiman compared treatment results of delayed surgery and readily available stenting, finding that patients who underwent surgery fared better. His study will inform future treatment decisions and policy in cardiac care.
2024	Dr. Cassidy-Matthews explored how Indigenous People who use drugs in BC experienced the COVID-19 pandemic and examined influences on vaccine uptake and acceptability. She found that a few relational principles underpinned most health decisions and experiences. These included emotional and spiritual connection, environmental stability, and equity.
2024	Dr. Yuchi studied air pollution, green space and dementia risk in Canada. Her work underscores the importance of further improvements to the built environment and air quality to reduce the burden of dementia in settings where air pollution levels are relatively low. Urban planning to incorporate greenery and parks may help to reduce dementia risk
2024	Dr. Nikiforuk studied how the coronavirus which causes COVID-19 infects cells in the upper human respiratory tract to find that people's risk of infection varies. This finding will be useful in controlling coronavirus transmission and designing new treatment strategies.
2024	Dr. Randall explored long-term patient satisfaction with total knee replacement. She found that 12% of participants were dissatisfied, particularly those with ongoing symptoms and unmet expectations. The main concern for patients was how well their new knee supported their daily lives. These findings have both clinical and research implications.
2024	Dr. Musoke evaluated the impact of two interventions to improve access to medicines in Uganda. He found that the benefits of such interventions were maintained over a long duration when implemented nationally. This knowledge will aid in the design of future interventions to improve access to medicines in Uganda and other countries.
2023	Dr. Desai revealed that despite better CF prognosis in recent years, people with CF still face substantial burden from lung impairment and other complications. Rising healthcare costs due to expensive medications pose additional challenges. These findings will help improve their service planning and resource allocation in the future.
2023	Dr. Nisingizwe investigated access to Hepatitis C testing and treatment in Rwanda and internationally. Her dissertation described HCV cascade of care, and patients' barriers to HCV care in Rwanda. Globally, she highlighted countries and regions with high and low access to HCV medicines and the effect of COVID-19 on HCV drug utilization.
2023	Dr. Chen unravelled relationships between diabetes medications and breast, colorectal, and pancreatic cancer risk, suggesting potential risk variations with common diabetes medications. Her study underscores the significance of understanding the long-term health impacts of prescription medications, advocating more research.

Sample Thesis Submissions

• Developmental profiles of children assessed for autism spectrum disorder at kindergarten and grade 4
• Promoting equitable access to digital sexually transmitted and blood borne infection testing interventions in British Columbia, Canada
• Evaluating access to medicines interventions in public and private not for profit health facilities in Uganda
• Investigating access to hepatitis C testing and treatment in Rwanda and beyond
• Improving referrals to rheumatologists for patients with inflammatory arthritis
• The social and economic impacts of cervical cancer on women and children in Uganda
• Exploring long-term patient satisfaction with total knee arthroplasty : a mixed methods study
• The Cedar Project : an exploration of Indigenous survivance, connection, and vaccine uptake amid concurrent public health emergencies experienced by urban Indigenous People who use drugs in British Columbia
• Examining childhood poverty and future developmental and academic outcomes of children in British Columbia : differences by poverty type and immigration background
• Assessing access to medicines in Canada and beyond before and during the COVID-19 pandemic
• Air pollution, green space and dementia risk in Canada
• Involvement of nasopharyngeal angiotensin converting enzyme 2 in severe acute respiratory coronavirus 2 infection and transmission
• Coronary revascularization and timing of treatment : comparative effectiveness of PCI and CABG in British Columbia
• Muddy molecules for pandemic protection : investigating the use of wetland sediment as a tool for the surveillance of avian influenza virus in wild waterfowl birds
• Methamphetamine use among people who use opioids : longitudinal patterns and the role of opioid agonist therapy

Related Programs

Same specialization.

• Master of Global Health (MGH)
• Master of Public Health (MPH)
• Master of Public Health and Master of Science in Nursing (MPH/MSN)
• Master of Science in Population and Public Health (MSc)

Same Academic Unit

• Master of Health Administration (MHA)
• Master of Health Science (MHSc)
• Master of Science in Occupational and Environmental Hygiene (MSc)

Further Information

Specialization.

The School of Population and Public Health (SPPH) offers both research-oriented and professional/course-based graduate programs.

Professional programs

• The Master of Public Health focuses on illness prevention and health promotion and integrates learning in epidemiology; biostatistics; the social, biological and environmental determinants of health; population health; global health; disease prevention and health systems management with skill-based learning in a practicum setting.
• The Master of Health Administration is a professional program for clinicians, administrators, researchers and managers who are seeking solutions to today’s complex health delivery issues. Take courses with a multi-disciplinary perspective in health systems, policies and management along with foundational business skills
• The Master of Science in Occupational and Environmental Hygiene (MSc OEH) program provides the skills and knowledge to anticipate, recognize, evaluate, and control chemical, physical and biological hazards in workplace and community environments.

Research-based Programs

• Master of Health Science (MHSc) applicants must have an MD or equivalent, including dentistry or veterinary medicine, and will learn skills that can be applied to their academic and clinical interests, bolstering their research abilities and opportunities.
• The Master of Science in Population and Public Health program teaches core knowledge and skills in epidemiological and biostatistical methods and allows students to gain research experience by applying methods to a thesis under the supervision of a faculty member. Students can pursue thesis research in a wide variety of topics related to the health of populations and the delivery of health services.
• The PhD program at SPPH is intended for students who wish to obtain advanced research training that will enable them to conduct independent investigative research.

UBC Calendar

Program website, faculty overview, academic unit, program identifier, classification, social media channels, supervisor search.

Departments/Programs may update graduate degree program details through the Faculty & Staff portal. To update contact details for application inquiries, please use this form .

Jorden Hendry

My experience with the Centre for Excellence in Indigenous Health solidified my decision to choose UBC for my graduate studies, as it offers a unique environment that values Indigenous perspectives and fosters meaningful research and leadership opportunities.

Hebah Hussaina

I completed both my Bachelor's and Master's degrees at UBC, and throughout those experiences, I became embedded within the community here. It was an easy choice to continue studying at UBC because of the love that I have for my community. Through my research, I want to give back to this community...

Zeina Waheed

UBC’s School of Population and Public Health provides excellent training in health economics, healthcare systems analysis, data analysis, statistics, epidemiology, and qualitative methods. Studying at UBC also provides me with the opportunity to work with my supervisor, Dr. Stirling Bryan, who is...

Katherine Hastings

Vancouver is home to one of the leading IYS networks internationally. When I sought out to learn more about IYS and their potential (something that did not exist in the States at the time), it felt like a perfect fit for my interests in youth mental health and health services research. The more...

Considering UBC for your graduate studies?

Here, you can choose from more than 300 graduate degree program options and 2000+ research supervisors. You can even design your own program.

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Doctor of Philosophy in Health Quality

Click here to learn how to apply!

The PhD in Health Quality (PhDHQ) will prepare experts who will improve the delivery of healthcare through teaching, developing new methodologies and theoretical frameworks, as well as testing innovation in the field of health quality. The PhDHQ program offers a collaborative approach to comprehend and address the complexities within the healthcare system. Graduates of the program will be prepared to take senior leadership roles in health quality portfolios in practice and policy settings across Canada and will also be educated to assume tenure track positions in university programs. While the degree is research intensive, it will also be grounded in pragmatism and will help prepare independent researchers for quality improvement research and developing leadership capabilities in health settings.

The PhDHQ program is a four-year, interdisciplinary program using a combination of synchronous and asynchronous study as well as interactive online videoconferencing. The PhDHQ program consists of five (5) courses in year one, including an internship over the summer months. The internship will be tailored to the learners’ interests and to broadening their perspectives on health quality. In the fall term of year two, students complete the comprehensive exam. In the winter and summer terms of year two (2) students will focus on the development of their thesis proposal and complete HQRS 905 Current Topics in Health Quality. After a successful oral examination of the thesis proposal, students submit their project for ethics review and then proceed to data collection, analysis, and writing. The thesis requires independent, original research and makes up at least two-thirds of the time normally required for the program. Upper year students are expected to visit campus at least once per year; students are required to attend the final thesis examination in person. Nurtured by close mentoring relationships with faculty supervisors, the Queen’s model is to ensure graduate students present and publish their research, and normally complete their program in 4 years.

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• DATA SCIENCE AND ANALYTICS
• Professional Programs

Master of Data Science and Analytics

• Graduate Diploma
• Specialization in Business Analytics
• Specialization in Data Science
• Specialization in Health Data Science and Biostatistics
• Specialization in Financial and Energy Markets Data Modelling
• Graduate Certificate
• Undergraduate Programs
• Undergraduate Major in Data Science
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• Bachelor of Commerce Concentration in Business Analytics

Application Portal Information

Winter 2025

• International Students- August 15 (For Masters in data Science and Analytics Only)
• Domestic Students- September 15 (For Certificate in Data Science and Analytics Only- Laddering into Graduate Diploma and Masters degree)
• Opening soon for both International and Domestic students. 

Lunch and Learn

Join our Lunch and Learn   to hear more about our professional programs in the rapidly evolving fields of Quantum Computing, Data Science and Analytics and Information Security. 

Our one hour session will cover all three of our professional programs and a complimentary lunch will be provided. Faculty members will also be on hand to meet prospective students and answer questions.

Next Lunch and Learn:

Date: Thursday, September 19, 2024 Time:  12:00 - 1:00 p.m. (MT)

Location:   University of Calgary Downtown Campus Room 234, Second Floor  906 - 8 Avenue SW, Calgary

Optimize your skills

Data scientists are among the top 15 tech jobs in demand through 2023, and the demand for digitally-skilled talent in Canada is projected to reach more than 305,000 in the next three years. Claim your spot in our country's growing tech economy with a multidisciplinary education from professionals in the Faculty of Science, Haskayne School of Business and the Cumming School of Medicine, and tailor your education to your interests. 

Upgrade your career

Earn a relevant graduate-level credential to advance your career.

Shift your skillset

Transition your career to tech with any background.

Get a head start

Recent grad? Stand out with a masters degree in a high-demand field.

Learn from leading experts

Design your experience.

Our curriculum was developed collaboratively to ensure that we provide every student with a multidisciplinary education in data science and analytics that leverages the expertise of leading researchers and instructors at the university. You will learn the fundamental concepts and tools of data science and analytics, machine learning, and artificial intelligence (AI), as you refine your professional and leadership skills while developing and applying your technical knowledge and abilities. You will be able to use concepts and tools across multiple contexts, industries, and sectors.

See a list of our available courses .

Masters Courses

The Master of Data Science includes the 24 units (eight 3-unit courses) from the Certificate and Diploma courses. All Masters students will take DATA 691 and then have a choice between pursuing a Professional Internship through DATA 693 or a Research Internship through DATA 695.

Data Science 691 - Integrated Topics in Data Science and Analytics

Provides a framework for students to initiate, perform and successfully complete a real-world project in Data Science and Analytics. This includes problem identification and formulation; discussion of legal, social, and ethical issues in data-driven projects; holistic processing and application of data; communication and data storytelling skills as well as leadership skills. In addition, students will be exposed to emerging topics in Data Science and Analytics.

Data Science 693 - Professional Internship in Data Science and Analytics

Students will integrate professional competencies and advanced analytical tools and apply them to a specific domain.

Data Science - Research Internship in Data Science and Analytics

Exposure to advanced data analytics methods and research methods applied to subdomains of data science, including business analytics and big data problems in healthcare.

Professional Internship

Internship is a self-driven 6-credit course requirement for students in the Master of Data Science and Analytics students' program. Students gain 6 – 16 weeks of paid industry work experience allowing them to integrate work experience into their degree, establish a professional network, and explore possible career paths and options.

What are the benefits?

Internships provide students the opportunity to maximize their student experience by offering paid, hands-on experience in an industry as part of their study program. As an intern, you have the opportunity to explore a career path, and discover personal strengths and interests, all while building a valuable network of professional contacts and securing strong references. 

Who is Eligible?

Students enrolled in the Master of Data Science and Analytics program are eligible upon the successful completion of 30 units of coursework. These consist of 12 units of the Graduate Certificate in Fundamental Data Science and Analytics, 12 units of coursework in one of the specialization areas, as well as DATA 691, prior to the internship course.  

For international students, a co-op work permit is required. At the start of the program, the Graduate Internship Coordinator provides a letter of support to include in their application for a co-op work permit. The processing time can be lengthy. To avoid delays, students should apply immediately after receiving their letter of support.   

What fees are associated with internship?

There is a tuition fee associated with the Internship course.  Please see the University Calendar for details .

There is no better time than the present to get training in Data Science and Analytics. The job field is still very new, so job opportunities are diverse. The program was an accelerated way to start building those skills. It allowed me to get a bit of a head start.

Tim Cruz, BComm’18

2021 graduate - Master of Data Science; 2019 graduate - Data Science & Analytics Diploma

We've compiled a list of the most-asked questions to help you find the information you need.

Our programs

We offer graduate certificate and diploma programs in Data Science and Analytics, as well as undergraduate minors to help set you up for success.

Master of Health Informatics

Advance your career as a health professional.

The Master of Health Informatics (MHI) program is designed for professionals with backgrounds in public health and/or health care who require more knowledge about computer science and health informatics.

Graduates can use this knowledge to identify, design and manage informatics solutions relevant to health and health systems.

Program overview 

• Designed for professionals with backgrounds in public health and/or health care: professionals who require more knowledge about computer science and health informatics in order to identify, design and manage informatics solutions relevant to health and health systems.
• You will learn from faculty who lead research in public health sciences and public health intervention design and evaluation. 
• Through the experiential learning of a practicum position, you will experience what it is like to use the knowledge, tools, and skills learned in the MHI Program in a real public health setting.
• The MHI program is flexible for the working professional and is completed online as a part-time or full-time student. 

The Master of Health Informatics program provides flexibility for the working professional and can be completed in 16 months to four years. It also features a four-month professional experience component.

Admission requirements

How to apply

Tuition costs

Living costs

Application deadline: february 1.

In order for an application to be considered, all required documentation, including academic references, must be submitted on or before this date.  Please aim to apply by January 18  to allow adequate time to upload supporting documents and ensure that your referees are aware of this firm deadline.  

NOTE: Due to the competitive nature of the professional programs at the University of Waterloo the ideal GPA for admission is based on the current pool of applicants and the previous years GPA cut-off. The minimum Graduate Studies application standard for admission is a CGPA of 3.0 or 75%. Successful applicants in the professional programs in 2023/2024 had an average GPA of 78%.

Course offerings and program sequence

The MHI program consists of 10 required courses (seven core courses, the practicum course, and two electives).

Click on the links below to view the course offerings and program sequence for part- and full-time students. These sequences are subject to change but can be used as reference for planning your future terms.

Professional practicum

Gain work experience by completing a 420-hour professional practicum at a hospital, provincial or federal governmental agency or non-governmental organization. You will w ork closely with the Experiential Learning and Communications Specialist to find a meaningful practicum that will provide you with an opportunity to apply your knowledge and skills in a professional setting and to connect with future employers.

The practicum can be completed on a full-time basis over one term or part time over two terms.

Previous practicum sites and projects

Site	Project
Canadian Institute for Health Information	Health data and information governance strategy
University Health Network	Use of data analytics in streamlining lung metastases
Public Health Ontario	Implementation of surveillance of healthcare associated infections in long-term care homes in Ontario
Brampton Civic Hospital	Unified communications project and meditech MSO project

Learn more about the practicum →

During her practicum placement for her  Master of Health Informatics  program, Acrifa Fears worked for a software company where she worked on a project that utilized digital health technology to monitor patients post-sugery at home wile faciltating administration of care and communication between patient and the healthcare system. Learn more about her practicum experience →

Funding and awards

A variety of scholarships, assistantships, and other forms of financial aid are available for graduate students in any professional graduate program.  Most of these awards are for full-time graduate students only.  

Learn more about funding and awards for professional programs →

Frequently asked questions

We've compiled the answers to the most common questions about the MHI program . Read through for helpful information about admissions, the practicum and more.

We have 19 data science PhD Projects, Programmes & Scholarships in Canada

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data science PhD Projects, Programmes & Scholarships in Canada

Dalhousie University

Halifax is home to leading tech research

Funded fellowship opportunities in Computer Science Education

Funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

Canada PhD Programme

A Canadian PhD usually takes 3-6 years. Programmes sometimes include taught classes and training modules followed by a comprehensive examination. You will then carry on to research your thesis, before presenting and defending your work. Programmes are usually offered in English, but universities in Québec and New Brunswick may teach in French.

Funded fellowship opportunities in Big Data Analytics, Artificial Intelligence and Machine Learning

Algorithms, data structures and computational geometry, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Funded PhD Project (Students Worldwide)

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Multimodal Data Integration in Livestock Farming through Artificial Intelligence

Funded fellowship opportunities in algorithms and bioinformatics, advanced algorithm design for inferring evolution, design of high-performance quantum thermoelectrics using experimental and computational techniques, hci and ubicomp impoving quality of life, experimental reactive fluid mechanics for aerospace applications, funded fellowship opportunities in systems, detection, prediction, and prevention of cyber-attacks on critical infrastructure, security for healthcare internet of things, enabling massive wireless connectivity for the internet of things, smartfarm: the ultimate animal care dashboard, text mining using deep language models and conversational ai.

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Health Data Analytics: Opportunities and Applications

Ubc micro-certificate.

Data analytics is a growth area within the health sector. Health systems worldwide are investing in data analytics infrastructure to enable service delivery improvements and increase efficiencies. Capitalizing on the potential of these innovations will require raising the level of data literacy and analytic capabilities of the health sector labour force.

The UBC Micro-certificate in Health Data Analytics: Opportunities and Applications is a part-time technical program developed by the UBC Department of Medicine and UBC Data Science Institute, in consultation with health sector leaders from government, academia and industry. It provides learners with career development and upskilling opportunities to fill the data literacy gap in our evolving data-driven health sector.

Blending foundational data analytics proficiency with health system context and data, this program will enable advancement or transition into a health data science role for professionals with a background in health care.

This micro-certificate was designed to complement existing knowledge of local health care systems and operations. By incorporating best practices and industry standards, the program equips learners with the analytics capabilities and tools needed to harness the power of data, and the confidence to start applying data analytics in their day-to-day work.

• Format: 100% online and instructor supported with real-time sessions
• Duration: Three courses of five weeks each, approximately 90 hours total
• Cost: $950 per course, $2,850 for the program

View courses & register

Information Session Recording

Learn more about the UBC Micro-certificate in Health Data Analytics. Meet program instructors, explore how data science is changing health care, and how you can apply your learning to benefit your organization.

Program details

Open All Accordions

What this program offers

Using real-world cases and data, the UBC Micro-certificate in Health Data Analytics: Opportunities and Applications offers students the hands-on training to gain confidence using data analytics techniques in a data-intense health system. The program blends foundational data analytics proficiency with health system context and data.

By the end of this program, you will be able to:

• recognize and discuss current applications of data science in the health system
• identify key considerations when working with health data in BC, including ethics, privacy and governance
• explain how the collection of data can improve clinical operations and the diagnosis, prognosis and treatment of diseases
• understand and discuss the basic concepts of data cleansing and data warehouses
• compare experimentally generated data and observational data
• identify and categorize the different types of data analysis questions
• discuss algorithmic bias, including its causes and consequences, to mitigate bias in machine learning
• demonstrate how data science storytelling can lead to policy and plan changes
• master the usage of WEKA software for data preprocessing, analysis, visualization and implementation of machine learning algorithms
• introduce principal component analysis and data clustering
• develop a basic understanding of natural language processing (NLP) and feature extraction techniques

Who is this program for

This program is designed for health care professionals and researchers, either clinical or operational, looking to enhance career performance and prepare for future opportunities, or those wanting to transition into an administrative or leadership role. No prerequisite knowledge of the course topics is required.

Roles that may benefit from this program include:

• pharmacists
• administrators

Each course costs $950. The total cost of the program is $2,850.

All fees are in Canadian dollars and subject to change. Fees are subject to GST where applicable. Fees may be paid by Visa®, Visa® Debit, Mastercard®, American Express®, money order or certified cheque.

For details on UBC’s payment policies, please see Refunds, Cancellations and Transfers .

Courses and dates

The micro-certificate program consists of three courses of five weeks each. Combined, the courses take approximately 90 hours to complete.

You can take the courses on their own, in any order. However, to earn your micro-certificate and gain the most value from the program, we recommend taking the courses in succession as they build upon one another:

• Introduction to the Big Data Era & Opportunities for Better Health Care (0114)
• Health Data and Visualization (0115)
• Health Data Analysis and Machine Learning (0116)

Required Courses

(0114) (0115) (0116)

Required Courses

Course name	Format	Next start date	Learn more

How we deliver this program

This part-time 100% online program is instructor supported and combines weekly real-time classes and independent study.

Outside of class, you can access online materials on your own time. Each week, you’ll have an opportunity to review readings and videos and apply your knowledge through quizzes, data analysis coding exercises and work-related mini projects. Students are also encouraged to contribute to and connect with one another on a discussion board.

Expected effort

Expect to spend approximately six hours per week completing all learning activities, including attending real-time sessions online.

Technology requirements

To take this program, you need access to:

• an email account
• a computer, laptop or tablet, using Windows, macOS or Linux
• the latest version of a web browser (or previous major version release)
• a reliable internet connection
• a video camera and microphone

In the Health Data Analysis and Machine Learning course, you will be using WEKA (Waikato Environment for Knowledge Analysis) data analysis software, which requires a computer less than 5 years old equipped with:

• a dual-core processor
• a minimum of 4GB RAM (8GB recommended)
• a minimum of 2GB free disk space
• Java 8 or higher software development platform installed

You are assessed on successfully completing weekly assignments and quizzes, as well as your contributions to discussion posts. These activities are marked using a proficiency scale, and your instructor provides you with informal feedback during online classes. You must achieve a minimum of 70% in each course to earn your micro-certificate.

While you are not assessed on your attendance of the real-time classes, we encourage you to attend so you don’t miss the opportunity to learn and interact with your instructor and other participants.

Meet your instructors

Our program is co-developed by UBC leaders in medicine and data science to meet the emerging analytic needs of professionals working in the health sector.

Anita Palepu, MD is a Professor, Eric W. Hamber Chair, and the Head of the Department of Medicine at the University of British Columbia and Providence Health Care. She is the Co-Lead of the Data Science and Health (DASH) Cluster.

Raymond Ng, PhD is a professor of Computer Science at the University of British Columbia and the Director of the Data Science Institute. He is also the holder of the Canadian Research Chair on Data Science and Analytics.

Guest instructors

• Brandon Wagar, PhD - Senior Director, Methodologies and Cross Sector Analysis, BC Ministry of Health; Adjunct Professor, School of Health Information Science, University of British Columbia
• Alexandra (Lexie) Flatt, MBA - Vice President, Pandemic Response & Chief Data Governance & Analytics Officer, Provincial Health Services Authority
• Eric Grafstein, MD - Regional Head, Department of Emergency Medicine & Chair, Regional Emergency Services Program, Providence Health Care & Vancouver Coastal Health
• Holly Longstaff, PhD - Director, Privacy and Access, Provincial Health Services Authority; Ethicist, BC Cancer Research Ethics Board
• Kimberlyn McGrail, PhD - Professor, School of Population and Public Health & Centre for Health Services and Policy Research, University of British Columbia; Scientific Director, Population Data BC & Health Data Research Network Canada
• Antonio Avina-Zubieta, MD, PhD - Senior Research Scientist, Arthritis Research Canada; Associate Professor, Department of Medicine, University of British Columbia
• Lianping Ti, PhD - Research Scientist & Health Administrative Data Lead, BC Centre for Substance Use; Assistant Professor, Department of Medicine, University of British Columbia
• Richard Lester MD - Director, Neglected Global Diseases Initiative & Associate Professor in Global Health, Department of Medicine, University of British Columbia; Scientific & Executive Director, WelTel International mHealth Society
• Teresa Tsang, MD - Director, UBC-VGH Artificial Intelligence Echo Core Lab & VGH-UBC Echo Lab; Clinician Scientist & Professor of Medicine, Department of Medicine, University of British Columbia; Co-Lead, Data Science and Health (DASH) Cluster
• Thalia Field, MD - Researcher, Djavad Mowafaghian Centre for Brain Health & Associate Professor, Department of Medicine, University of British Columbia
• Jennifer MacGregor, MBA - Vice President, Digital Patient and Provider Experience, Fraser Health Authority
• Jonathan Simkin, PhD - Scientific Director, BC Cancer Registry, BC Cancer
• Kevin Doerksen, PhD - Director, Department of Organizational Performance, BC Emergency Health Services
• Aline Talhouk, PhD - Assistant Professor, Department of Obstetrics and Gynecology, University of British Columbia
• Yulia Egorova, PhD - Trainee Lead, Data Science and Health (DASH) Cluster
• Robert Bergen, PhD - Health Data Scientist, Data Science Institute, University of British Columbia
• Daniel Holmes, MD - Head & Medical Director, Department of Pathology and Laboratory Medicine, St. Paul’s Hospital; Clinical Professor, Department of Pathology and Laboratory Medicine, University of British Columbia
• Dr. Daniel Chen - Postdoctoral Research and Teaching Fellow, Master of Data Science, University of British Columbia
• Lynn Hancock, PhD - Director, Strategic Priorities Research and Reporting, Health Workforce Planning and Implementation, BC Ministry of Health

Accreditation

Health Data Analytics: Opportunities and Applications  is approved by UBC CPD for credits.  

The Division of Continuing Professional Development, University of British Columbia Faculty of Medicine (UBC CPD) is fully accredited by the Continuing Medical Education Accreditation Committee (CACME) to provide CPD credits for physicians. This activity is an Accredited Self-Assessment Program (Section 3) as defined by the Maintenance of Certification Program of the Royal College of Physicians and Surgeons of Canada, and approved by UBC CPD. You may claim a maximum of 75 hours (credits are automatically calculated). This one-credit-per-hour Assessment program meets the certification criteria of the College of Family Physicians of Canada and has been certified by UBC CPD for up to 75 Mainpro+® credits.  Each physician should claim only those credits accrued through participation in the activity.

SAP ID: 00016410 CFPC Session ID: 202500-001

The discussion questions and assignments promoted thinking about the content in real life scenarios, which helps to understand the content. I liked that the presenters had a lot of knowledge of the subjects. - Student, Introduction to the Big Data Era & Opportunities for Better Health Care

Accredited by UBC CPD

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PhD in administration - Data Science

Phd in administration — data science.

• Tuition fees and Funding

Are you planning on a career in academia or business in the field of data science? Join a community of professors and researchers with valuable, recognized expertise.

Your PhD in short

• Offered by HEC Montréal jointly with Concordia and McGill universities and the Université du Québec à Montréal (UQAM). This partnership gives you access to resources (faculty and courses) rarely available elsewhere in the world.
• Full-time program allowing you to complete your studies in four or five years.
• Tuition fees waived and competitive funding for the first four years of your studies.

For a fascinating career

“Whereas many PhD programs aim exclusively at academic careers, HEC’s approach satisfies both academic interests and professional education.” Walid Mathlouthi, PhD. Data Science Consultant

“The PhD program allowed me to build solid knowledge of data science methods that enables me to solve complex problems with rigor and success, and also to contribute to innovation in the industry.” Marie-Hélène Roy, PhD. Lead Data Scientist – Age of Learning, California

“The teaching and support I received during my PhD studies at HEC Montréal were of such high quality that I couldn’t help but succeed.” Ahlem Hajjem, PhD. Professor at ESG UQAM

Among the best

The professors associated with this doctoral specialization have authored over 400 scientific papers in the top journals, including:

• Bioinformatics
• Journal of the American Statistical Association
• Statistical Methods in Medical Research

Be part of research innovations

Many of the professors in this specialization are members of MILA , Montréal’s world-renowned centre for artificial intelligence research.

Varied research interests

Methodology.

• Artificial intelligence
• Bayesian statistics
• Big data mining
• Data mining
• Deep learning
• Machine learning
• Non-parametric methods
• Recommendation systems

Applications

• Analytics of urban environments
• Network analysis
• Traffic safety
• User experience (UX)

Our PhD students and candidates

See the list of students in this specialization on Google Scholar

An exceptional research milieu

The eleven professors mainly associated with the doctoral specialization in Data Science have substantial research funds at their disposal to assist students. 

Eight of them hold chairs or professorships:

• FRQ-IVADO Chair in Data Science: Professor Aurélie Labbe
• Canada CIFAR Chair in Artificial Intelligence: Professor Laurent Charlin
• Canada CIFAR Chair in Artificial Intelligence: Professor Jian Tang
• Research Professorship in Complex Networks: Professor Gilles Caporossi
• Research Professorship in Data Science: Professor Denis Larocque
• Research Professorship in Statistics: Professor Debbie J. Dupuis
• Professorship in Pedagogical Innovation in Active Learning of Statistics in Management: Professor Chantal Labbé
• Professorship in Pedagogical Innovation in the Gamification of Learning for Data Science: professor Jean-François Plante

Marc Fredette is principal collaborator at the NSERC-Prompt Industrial Research Chair in User Experience .

Researchers in this specialization work closely with several research groups and knowledge transfer hubs, including:

• Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT)
• Centre de recherches mathématiques (CRM) (Mathematics research centre)
• Group for Research in Decision Analysis (GERAD)
• Montreal Institute for Learning Algorithms (MILA)

In addition, HEC Montréal is an institutional member of the Canadian Statistical Sciences Institute (CANSSI) .

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•   Questions about our PHD Program?
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Health Data Science - PHD

Program Guide

The PhD Program in Health Data Science trains the next generation of data science leaders for applications in public health and medicine. The program advances future leaders in health and biomedical data science by: (i) providing rigorous training in the fundamentals of health and biomedical data science, (ii) fostering innovative thinking for the design, conduct, analysis, and reporting of public health research studies, and (iii) providing practical training through real-world research opportunities at research centers and institutes directed by departmental faculty such as the Biostatistics Center (BSC), the Computational Biology Institute (CBI), and the Biostatistics and Epidemiology Consulting Service (BECS).

The PhD program consists of two concentrations; Biostatistics & Bioinformatics Concentration. Biostatistics is the science of designing, conducting, analyzing, and reporting studies aimed at advancing public health and medicine. Bioinformatics is the science of developing and applying computational algorithms and analysis methodologies to big biological data such as genetic sequences. Together they are foundational sciences for public health research and decision-making and essential to educating the next generation of leaders in health and biomedical data science.

The program takes advantage of the rich biostatistical and bioinformatics resources at GW and in the Nation’s Capital. Faculty in the Department of Biostatistics and Bioinformatics are engaged in a diverse research portfolio that includes areas such as diabetes, infectious diseases, mental health, maternal-fetal medicine, cardiovascular disease, emergency medicine, and oncology. Methodological interests of the faculty include the design and analyses of clinical trials including group-sequential and adaptive design, SMART trials, pragmatic trials, multiple testing, and benefit: risk evaluation; machine learning; meta-analyses; missing data; randomization tests, longitudinal data; the use of real-world data including electronic medical records; and research in biostatistics education methodologies. The Washington DC area is a hub for biostatisticians and bioinformaticians in government and industry, providing a rich source of adjunct faculty with relevant experience.  Specifically, the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) have considerable human resources in these disciplines, many with world-class reputations. Several leading biostatisticians from the NIH are currently serving on doctoral committees and teach courses in the Milken Institute School of Public Health (GWSPH).

The program features a modernized applied curriculum, unique in its emphasis on cutting-edge data science techniques while retaining the rigor of traditional Biostatistics and Bioinformatics programs. The program prepares students to be independent researchers and effective collaborators in interdisciplinary studies.

APPLICATION DEADLINE: DECEMBER 1

Program Co-Directors:  Dr. Keith Crandall  (Bioinformatics Concentration)

Dr. Guoqing Diao  (Biostatistics Concentration)

Dr. Toshimitsu Hamasaki  (Biostatistics Concentration)

GWSPH Doctoral programs admit students for the Fall term each academic year. Applications will be accepted beginning in August and are due no later than December 1st for the next matriculating cohort beginning in the following Fall term.  Find GWSPH graduate admissions information  here .

All applicants for the Biostatistics Concentration are required to submit current GRE scores (within five years of matriculation date). Applicants for the Bioinformatics Concentration are strongly encouraged to submit a GRE score.

Meeting the minimum requirements does not assure acceptance. Applicants must provide evidence of the completion of their undergraduate and/or graduate work before registration in GWSPH is permitted.

Concentration-Specific Prerequisites

Additional advanced courses in mathematics and calculus-based probability are encouraged but not a requirement for admission.

Transfer Credits

Graduate courses taken prior to admission while in non-degree status may not be transferable into GWSPH programs. The PhD program is designed to serve students coming directly from an undergraduate degree. Students completing a master’s degree prior to admission to the PhD degree program may be eligible to transfer up to 24 credits toward the PhD coursework requirements. Depending on how many transfer credits are accepted, at minimum, 48 credits of additional coursework and dissertation research will be required.

PUBH 6080  | Pathways to Public Health (0 credits) PUBH 6421 | Responsible Conduct of Research (1 credit) PUBH 6850   | Introduction to SAS for Public Health Research (1 credit) PUBH 6851   | Introduction to R for Public Health Research (1 credit) PUBH 6852   | Introduction to Python for Public Health Research (1 credit)  PUBH 6860   | Principles of Bioinformatics (3 credits)  PUBH 6886   | Statistical and Machine Learning for Public Health Research (3 credits) PUBH 8099 | PhD Seminar: Cross Cutting Concepts in Public Health (1 credit) NOTE: In 23-24, PUBH 8099 was updated to PUBH 8001 PUBH 8870 | Statistical Inference for Public Health Research I* (3 credits)

CORE TOTAL: 14 CREDITS

SPH Course Descriptions

PUBH 6866   | Principles of Clinical Trials (3 credits)  PUBH 6869   | Principles of  Biostatistical Consulting (1 credit) PUBH 6879 | Propensity Score Methods for Causal Inference in Observational Studies (3 credits) PUBH 6887   | Applied Longitudinal Data Analysis for Public Health Research (3 credits) PUBH 8871   | Statistical Inference for Public Health Research II* (3 credits) PUBH 8875 | Linear Models in Biostatistics* (3 credits)  PUBH 8877   | Generalized Linear Models in Biostatistics* (3 credits) PUBH 8878 | Statistical Genetics (3 credits) PUBH 8879 | An Introduction to Causal Inference for Public Health Research (3 credits) PUBH 8880 | Statistical Computing for Public Health Research (3 credits) STAT 6227   | Survival Analysis (3 credits)

BIOSTATISTICS CONCENTRATION-SPECIFIC TOTAL: 28 CREDITS

* Courses are basis of comprehensive exam for the Biostatistics concentration.

PUBH 6854 | Applied Computing in Health Data Science (3 credits) PUBH 6859 | High Performance Cloud Computing (3 credits)  PUBH 6861   |  Public Health Genomics (3 credits)  PUBH 68 84   |  Bioinformatics  Algorithms and Data Structure (3 credits)  PUBH 8885   | Computational Biology (3 credits) 

BIOINFORMATICS CONCENTRATION-SPECIFIC TOTAL: 15 CREDITS

• Applied Biostatistics Concentration: 12 credits minimum
• Bioinformatics Concentration:  18 credits minimum

Both concentrations:  elective selections must include at least*:

• 3 Credits in Biostatistics 
• 3 Credits in Bioinformatics
• 3 Credits in a Cognate Area

All students are expected to work with their Advisor in the selection of their Elective coursework.

*Pre-approved elective courses are shown in the program guide for each category. 

 BIOSTATISTICS ELECTIVES MINIMUM: 12 CREDITS BIOINFORMATICS ELECTIVES MINIMUM: 18 CREDITS

PRACTICUM/TEACHING RESEARCH GTAP**   |   GradTeachingAsst Certification (This includes UNIV 0250 - Graduate Assistant Certification Course (1 credit) (both concentrations) (0; 1 credit) PUBH 8283 | Doctoral Biostatistics Consulting Practicum (Biostatistics concentration only) (2 credits) PUBH 8413 |  Research Leadership (both concentrations (1 credit)

** This is a requirement for TAs .

DISSERTATION RESEARCH PUBH 8999 |  Dissertation Research (varies by concentration - 12 minimum credits)

BIOSTATISTICS PRACTICUM/RESEARCH: 12-15 CREDITS BIOINFORMATICS PRACTICUM/RESEARCH: 12-24 CREDITS

Professional Enhancement

Students in degree programs must participate in eight hours of Professional Enhancement. These activities may be Public Health-related lectures, seminars, or symposia related to your field of study.

Professional Enhancement activities supplement the rigorous academic curriculum of the SPH degree programs and help prepare students to participate actively in the professional community. You can learn more about opportunities for Professional Enhancement via the Milken Institute School of Public Health Listserv, through departmental communications, or by speaking with your advisor.

Students must submit a completed  Professional Enhancement Form  to the student records department  [email protected] .

Collaborative Institutional Training Initiative (CITI) Training

All students are required to complete the Basic  CITI training module  in Social and Behavioral Research prior to beginning the practicum.  This online training module for Social and Behavioral Researchers will help new students demonstrate and maintain sufficient knowledge of the ethical principles and regulatory requirements for protecting human subjects - key for any public health research.

Academic Integrity Quiz

All Milken Institute School of Public Health students are required to review the University’s Code of Academic Integrity and complete the GW Academic Integrity Activity.  This activity must be completed within 2 weeks of matriculation. Information on GWSPH Academic Integrity requirements can be found  here.

Past Program Guides

Students in the PhD in Health and Biomedical Data Science program should refer to the guide from the year in which they matriculated into the program. For the current program guide, click the "PROGRAM GUIDE" button on the right-hand side of the page.

Program Guide 2023-2024 Program Guide 2022-2023 Program Guide 2021-2022

Lizhao (Agnes) Ge Email:   [email protected] Start year: 2021

Lizhao was born and raised in Zhejiang, China. She came to the United States for undergraduate studies at the University of Iowa, where she obtained a BS in Mathematics and a BBA in Finance, and a minor in Music. She earned a Master of Applied Statistics from the Pennsylvania State University and worked there as a Statistical Consultant after graduation. She joined the Antibacterial Resistance Leadership Group (ARLG) at the George Washington University Biostatistics Center as a biostatistician in 2020 and started her PhD journey in the Health and Biomedical Data Science (Applied Biostatistics track) in 2021. Her research interests are clinical trial designs, and application of the Desirability of Outcome Ranking (DOOR) in biomedical studies.

Yijie He Email:   [email protected] Start year: 2021

Yijie was born in China. Before coming to the George Washington University, he received a BS degree in Bioengineering from University of California San Diego and an MS degree in Biostatistics from Duke University. He is currently a PhD student in Health and Biomedical Data Science, in the Applied Biostatistics track, and he also works at the George Washington University Biostatistics Center as a biostatistician. His current research interests include clinical trials, high-dimensional data, and data science.

Shiyu Shu Email:  [email protected] Start year: 2021

Shiyu (Richard) was born and raised in Dalian, China, and has been studying in the United States for the last 7 years. He obtained a BA in Mathematics and in Economics from Vassar College, during which he spent one semester as an exchange student at St Edmund Hall, Oxford University. He then received a Master of Statistical Practice from Carnegie Mellon University, and worked as a data analyst for a healthcare organization in rural Arizona during the peak of the COVID pandemic. The work experience motivated him to pursue a career in public health, and to continue his PhD studies in the Health and Biomedical Data Science program at GWU. He is currently a biostatistician working in the Diabetes Prevention Program team (DPP) at the Biostatistics Center, under the supervision of Dr. Marinella Temprosa. His current research interests include machine learning/data science, genomics data and survival analysis.

Shanshan Zhang Email:  [email protected] Start year: 2021

Shanshan was born and raised in China. She earned a Bachelor of Medicine and a Master of Science in Cell Biology from China Medical University. When she came to the United States in 2018, she transferred her interests to public health, since a doctor can save individuals, whereas a public health expert can save lives on a population level. She obtained a second graduate degree, an MS in Biostatistics from the George Washington University. Shanshan hopes that she can make contributions to the field of public health, especially in designing and conducting clinical trials during the PhD program, and can work as an outstanding biostatistician in the future.

Recent Publications:

Qiongfang Wu, Leizhen Xia, Lifeng Tian, Shanshan Zhang, Jialyu Huang. Hormonal replacement treatment for frozen-thawed embryo transfer with or without GnRH agonist pretreatment: a retrospective cohort study stratified by times of embryo implantation failures. Accepted by Frontiers in Endocrinology. 5 January 2022

Shanshan Zhang. Biostatistics in Clinical Decision Making: What can We Get from a 2× 2 Contingency Table. E3S Web of Conferences (Vol. 233). EDP Sciences. December 2020

Qiqiang Guo, Shanshan Wang, Shanshan Zhang, Hongde Xu, Xiaoman Li, et al. ATM‐CHK 2‐Beclin 1 Axis Promotes Autophagy to Maintain ROS Homeostasis Under Oxidative Stress. The EMBO Journal, 39(10), e103111. 18 March 2020

PhD in Bioinformatics Students

Mahdi Baghbanzadeh Email:  [email protected] ;  [email protected] Start year: 2021

Mahdi Baghbanzadeh is a Ph.D. student in the health and biomedical data science program at the Milken Institute School of Public Health at the George Washington University. Mahdi received his MS in Mathematical Statistics from Shiraz University in 2012, and his BS in Statistics from Shahid Beheshti University in 2010.  Before joining GWU, he had the experience of 7 years performing in an analytical role ranging from data analyst to senior data scientist in multiple companies. His research interests are applying machine learning algorithms in analyzing omics data, developing tools for studying the genotype-phenotype association studies, and the effects of different medications on a certain disease.

Publications:

Baghbanzadeh, Mostafa; Simeone, F. C.; Bowers, C. M.; Liao, K.-C.; Thuo, M. M.;  Baghbanzadeh, Mahdi ; Miller, M.; Carmichael, T. B.; Whitesides, G. M.*  “Odd-even effects in charge transport across n-alkanethiolate-based SAMs”   Journal of American Chemical Society ,  2014 ,  136 , 16919–16925.

Mahdi Baghbanzadeh , Dewesh Kumar, Sare I. Yavasoglu, Sydney Manning, Ahmad Ali Hanafi-Bojd, Hassan Ghasemzadeh, Ifthekar Sikder, Dilip Kumar, Nisha Murmu, Ubydul Haque*  “Malaria Epidemics in India: Role of Climatic Condition and Control Measures”   Science of the Total Environment ,  2020 ,  712 , 136368.

Peeri, Noah C., Nistha Shrestha, Md Siddikur Rahman, Rafdzah Zaki, Zhengqi Tan, Saana Bibi,  Mahdi Baghbanzadeh , Nasrin Aghamohammadi, Wenyi Zhang, and Ubydul Haque.  " The SARS, MERS and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? "   International Journal of Epidemiology ,  2020 ,  49 , 717-726.

Md Siddikur Rahman, Ajlina Karamehic-Muratovic,  Mahdi Baghbanzadeh , Miftahuzzannat Amrin, Sumaira Zafar, Nadia Nahrin Rahman, Sharifa Umma Shirina, Ubydul Haque,  “Climate change and dengue fever knowledge, attitudes and practices in Bangladesh: a social media–based cross-sectional survey” ,  Transactions of The Royal Society of Tropical Medicine and Hygiene ,  2021 ,  115 , 85-93.

Nistha Shrestha, Muhammad Yousaf Shad, Osman Ulvi, Modasser Hossain Khan, Ajlina Karamehic-Muratovic, Uyen-Sa D.T. Nguyen,  Mahdi Baghbanzadeh , Robert Wardrup, Nasrin Aghamohammadi, Diana Cervantes, Kh. Md Nahiduzzaman, Rafdzah Ahmed Zaki, Ubydul Haque,  “ The impact of COVID-19 on globalization” ,  One Health ,  2020 , 100180.

Osman Ulvi, Ajlina Karamehic-Muratovic,  Mahdi Baghbanzadeh , Ateka Bashir, Jacob Smith, Ubydul Haque, “ Social Media Use and Mental Health: A Global Analysis ”,  Epidemiologia,   2022 , 3 (1), 11-25 .

Ranojoy Chatterjee Email:  [email protected] ;  [email protected] Start year: 2021

Ranojoy is originally from Kolkata, India. He got his B.Tech in Computer Science from WBUT and has an MS in Computer Science from Kansas State University, specializing in recommendation systems using a multi-armed bandit approach. After graduation he worked at Bellwethr, Inc developing a retention engine which was later patented by the company. After his brief stint in industry, he worked as a research specialist in Rahlab to develop machine learning tools for analyzing Covid-19 data. His current research interests are graph neural networks, single cell data and prediction systems in biomedical data science.  

Amritphale, A., Chatterjee, R., Chatterjee, S.  et al.  Predictors of 30-Day Unplanned Readmission After Carotid Artery Stenting Using Artificial Intelligence.  Adv Ther   38,  2954–2972 (2021).  https://doi.org/10.1007/s12325-021-01709-7

Chow JH, Rahnavard A, Gomberg-Maitland M, Chatterjee R, et al. Association of Early Aspirin Use With In-Hospital Mortality in Patients With Moderate COVID-19.  JAMA Netw Open.  2022;5(3):e223890. doi:10.1001/jamanetworkopen.2022.3890

Clark Gaylord Email:  [email protected] Start year: 2021

After receiving M.S. degrees in Mathematics and Statistics from the University of Virginia and Virginia Tech, respectively, Clark has had a career in information technology, network security, and research computing. Over the last 20 years, Clark has led the design and operation of many research computing and big data research systems, and is a consulting statistician on several research projects. While at Virginia Tech, Clark taught several courses in Statistics, Data Science, and Networking. A PhD candidate in GW's Health and Biomedical Data Science, Bioinformatics Track, Clark is also Director of Research Technology Services in GW IT.

CAAREN:  https://www.caaren.org/clark-gaylord GW High Performance Computing:  https://www.hpc.arc.gwu.edu/

Erika Hubbard Email:  [email protected] Start year: 2021

Erika was born and raised in Fairfax County, Virginia (NOVA) and earned her BSc in Biomedical Engineering with minor concentrations in Applied Mathematics and Engineering Business from the University of Virginia. Upon graduation she went on to intern and work for AMPEL BioSolutions, LLC in Charlottesville, VA, researching autoimmune and inflammatory diseases, primarily systemic lupus erythematosus (SLE). As a dual member of the systems biology and bioinformatics teams at AMPEL she developed an interest in leveraging genomics data to gain insights into mechanisms of autoimmune disease pathogenesis. She continues to work with AMPEL to study lupus and translate findings into novel clinical tools to further precision medicine. 

Hubbard EL, Pisetsky DS, Lipsky PE. Anti-RNP antibodies are associated with the interferon gene signature but not decreased complement levels in SLE. Ann Rheum Dis [Epub ahead of print: 3 Feb 2022]. doi:  https://doi.org/10.1136/annrheumdis-2021-221662

Hubbard EL, Grammer AC, Lipsky PE. Transcriptomics data: pointing the way to subclassification and personalized medicine in systemic lupus erythematosus. Curr Opin Rheumatol [Internet]. 2021 Nov 1;33(6):579-85. doi:  https://doi.org/10.1097/bor.0000000000000833   

Daamen AR, Bachali P, Owen KA, Kingsmore KM, Hubbard EL, Labonte AC, et al. Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway. Sci Rep [Internet]. 2021 Mar 29;11(1):7052. doi:  https://doi.org/10.1038/s41598-021-86002-x

Hubbard EL, Catalina MD, Heuer S, Bachali P, Geraci NS, et al. Analysis of gene expression from systemic lupus erythematosus synovium reveals myeloid cell-driven pathogenesis of lupus arthritis. Sci Rep [Internet]. 2020 Oct 15;10(1):17361. doi:  https://doi.org/10.1038/s41598-020-74391-4

Xinyang Zhang Email:  [email protected] ;  [email protected] Start year: 2021

Xinyang was born and raised in Jiangsu, China. Before coming to George Washington University, she obtained her MS in Data Informatics at the University of Southern California, Los Angeles. For now, she started her Ph.D. journey in Health and biomedical data science (Applied Bioinformatics track) and works for the Computational Biology Institute (CBI) as a Research Assistant. Her research interest focuses on microbiome analysis, omics data for the COVID-19, and reference-grade pathogen sequences database construction.  

Dalla Lana School of Public Health

• PhD: Epidemiology
• Our Programs
• Doctor of Philosophy (PhD)

Degree Overview

This program aims to develop excellent epidemiologists, able to work, teach and conduct research on contributors to health; disease, disability and death; and effective measures of prevention.

The overall goal of the program is to enable graduates to acquire the necessary scientific knowledge and methodological skills to become independent researchers in epidemiology.  Graduates with a PhD in epidemiology are expected to have developed the skills which enable them to:

• evaluate the scientific literature with respect to epidemiologic concepts, theoretical hypotheses, designs, methods, analyses and interpretation;
• develop theoretical formulations and testable hypotheses from concepts in the literature or epidemiological observations, and propose research questions and design and write research proposals;
• understand the practical and scientific implications of epidemiological research designs and the associated methodological and analytical techniques;
• identify and evaluate available data for addressing specific research questions;
• evaluate strengths and weaknesses of data collection methods, develop methods appropriate for answering specific research questions, and assess the measurement properties of data collection tools;
• address ethical issues related to epidemiologic studies;
• appreciate the policy implications of epidemiologic research; and,
• write and defend a doctoral dissertation which makes a contribution to the scientific literature.

Click here to view PhD Competencies

Admission Requirements

• Applicants generally are expected to hold a master’s degree in epidemiology or a master’s degree in a related field with strong course work in epidemiology and biostatistics.
• Applicants are expected to have prior research experience which may be demonstrated through the completion of a master’s thesis, supervised research practicum, or other research experience, and which includes independent contributions to scientific publications.
• Applicants should have practical experience and reasonable expertise using standard statistical software packages.
• Click here for information regarding the application process.

Successful applicants will have research interests congruent with those of one or more members of faculty, and may have identified a possible primary or co-supervisor, prior to admission.  Admission may otherwise be conditional upon identifying a supervisor.  Thus, applicants are strongly encouraged to seek out potential supervisors, and discuss with them the possibilities, prior to applying to the degree program.  Applicants should note that identifying a potential supervisor does not guarantee admission.

Course Requirements

Course Requirements (3.5 FCE)

Required Courses (3.0)

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This course requires enrollment during the first 2 years of study to achieve credit. After the second year, upper year students and faculty supervisors are expected to attend and participate.	0.5
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Elective Courses (0.5)

Students are best served if their elective courses form part of a coherent package of experience. In this light, students are encouraged to choose elective courses that relate to the theme of their dissertation. For example, advanced methodological courses might be appropriate for a dissertation which involves highly complex statistical analysis; pathology courses for a dissertation which focuses more on disease process; bioethics courses for a dissertation on genetic epidemiology. Electives also may fill gaps in overall training and experience: A student with a largely social sciences background might benefit from health professional level pathology courses; a student with substantial bench-sciences training, who is interested in disease screening, might consider courses in behavioural sciences, health economics, or health policy. Students are encouraged to discuss the selection of appropriate electives with their Supervisory Committees.

Emphasis in Artificial intelligence and Data Science

Students in the PhD program in the Epidemiology field of study have the option to complete an emphasis by completing appropriate coursework in a given area. The emphasis requirements will also count toward, but may exceed, the 4.0 full-course equivalent (FCE) field requirement.

Course Requirements: Emphasis in Artificial Intelligence and Data Science (1.5 FCE)

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Other course(s) approved by the Program Director

Qualifying Examination

The qualifying examination is an in-class written exam:

• This examination is held June of the first academic year and details are provided to students during the first year.
• The examination is designed to test competence in the concepts, principles, data sources, and content of epidemiology, and the ability to apply these concepts and principles critically.
• The examination may include multiple choice and short answer questions.
• An Examination Committee will mark the examination, blind to the identity of the student. A passing grade is 70%. Students who achieve higher percentages will be informed that they have received grades of Honours (90%+) or High Pass (80-89%).

The written qualifying can be fulfilled after the following required courses are complete:

CHL5404H: Research Methods I (0.5) CHL5406H: Quantitative Methods for Biomedical Research (0.5) CHL5408H: Research Methods II (0.5) CHL5424H: Advanced Quantitative Methods in Epidemiology (0.5)

PhD Proposal Defense

The PhD proposal defense is a requirement for candidacy and should be completed by December of the second year.  The proposal defense can be done during the first year of study  with the approval of the Program Director. The purpose of the proposal defense is to:

• Ensure that proposed research will result in a successful PhD dissertation.
• Strengthen the thesis question, design, and methods through critical feedback.
• Assess the students’ ability to conduct independent and original research.
• Assess sufficient content/substantive knowledge base relevant to their thesis topic.
• Provide a formal approval to proceed with the dissertation research.

Format: The proposal will include a brief and cogent review of the literature, justification of the research question, the objectives and hypotheses, design, data collection or data sources, proposed analysis strategies, timetable, ethics, and potential problems or issues. The proposal will conclude with references in proper bibliographic format. The proposal also will include a concise statement of the student’s role in the development and conduct of the research. A title page, with word count, will include the names of the Supervisor and other Supervisory Committee members. The proposal will be printed using a 12-point font, and limited to 10 single-spaced pages. The bibliography and title page are not included in the page or word counts. Appendices should be kept to a minimum.

Defense for approval of PhD proposal:

The proposal defense consists of a written outline of the dissertation proposal and an oral presentation. The completion of this process also counts as the protocol approval, which is required for candidacy. The following elements will be assessed:

• The literature review is comprehensive and specific to the content area;
• The proposed work demonstrates scholarly impact and innovation with respect to methods and/or substantive contribution;
• Clarity of research question/objectives
• Completeness and relevance to study design/research plan
• Rationale for approach and methodology
• Appropriateness of research design
• Appropriateness of research methods and statistical analyses
• Feasibility of research approach including power calculation as appropriate
• Requirement, timeline, preliminary data etc.
• Anticipation of difficulties/limitations and plans for management
• Ethical considerations
• The project is adequate and appropriate for a PhD dissertation and manageable within the time-frame and expectations of the PhD program.

The proposal presentation must be attended by the student, the Supervisory Committee and one external reviewer approved by the Program Director. The presentation will be advertised within the Graduate Department of Public Health Sciences, and students and faculty are encouraged to attend.  The external reviewer must be a Full or Associate member of SGS, ideally has research supervisory experience at the doctoral level, and must have specific research expertise in the dissertation topic or methods. The reviewer should have had no previous involvement with the development of the proposal under review.

Process for evaluation:

• The student’s Supervisory Committee approves the written proposal at least three weeks before the anticipated date of proposal defense.
• The student contacts the Program Director, with a copy to the Administrative Assistant, to give notice that the proposal is ready for defense, together with the name, email and brief rationale for the external reviewer. As a reminder, the reviewer must have an SGS appointment at the University of Toronto. The Program Director will approve the external reviewer via email.
• The Supervisor contacts reviewer and committee to arrange the date/time of the presentation, and informs the program Administrative Assistant of the arrangements.
• The Administrative Assistant reserves a room and any required audiovisual equipment specified by the student, and posts notices on bulletin boards and e-mail, including a confirmatory e-mail to the reviewers and Supervisory Committee.
• The student distributes the proposal to the external reviewer, Supervisory Committee members, and Administrative Assistant, three weeks before the date of the proposal defense.
• The proposal defense will begin with a 20-minute presentation of the research proposal by the student, followed by a period of questions and discussion. Presentation questions are posed to the student in two rounds, with approximately 10 minutes allotted to each reviewer per round, with the reviewer taking the lead in the questions. The Supervisor will chair the proceedings and act as timekeeper. The question period will typically be expected to last 60 to 80 minutes. The Supervisor will take notes of all issues raised.
• At the end of formal questioning, the student and other attendees not part of the review panel will leave the room, and the reviewer and Supervisory Committee will have a general discussion of four elements (I – IV) outlined above. The reviewers will rate the performance of the student using a standardized form and an Accept/Provisional Acceptance/Not Accepted decision will be reached. The Supervisor and external reviewer will take note of the feedback and prepare a summary of the recommendations to share with the student.  Typically, the Supervisor will take notes, on the form during the defense, and email to the external reviewer for final review before sending to the student.

The following outline the implications for the evaluation:

Approval: The student may proceed with dissertation work and remaining program progression, taking note of all feedback received during the protocol defense and in consultation with the Supervisor considering minor amendments to their doctoral research accordingly. This candidacy requirement has been met.

Provisional Approval: The student must create a point-by-point response to the concerns/issues raised and make changes to the proposal within 60 days of the proposal defense. Once the Supervisory committee has approved the revisions, the proposal must be submitted to the Program Director and Administrative Assistant as a final record. An approval will then be recorded for candidacy.

Not approved: Non-approval indicates that the performance was inadequate and/or the protocol has major deficiencies according to the IV domains. In the event that the student is not approved on the first attempt, the student will be permitted one more attempt. Failure of the second attempt will result in a recommendation for program termination.

• At the conclusion of the discussion, the student will be invited into the room to learn the general outline of the committee’s decision. The decision and the completed form must be conveyed to the Program Director and Administrative Assistant within 1 week of the defense.

Supervision

Click here to view the SGS Supervision Guidelines for Students.

Beginning prior to admission, and with the assistance of the Program Director, the applicant will explore supervisory possibilities: a faculty member with an appointment in the Division of Epidemiology who has a Full appointment in the School of Graduate Studies (SGS), and who conducts epidemiological research. In some instances, the student and the Program Director will identify both a primary and a co-supervisor. The co-supervisor generally will be a faculty member with an Associate appointment in the SGS. The faculty supervisor may be confirmed prior to beginning the program, and generally will be in place by the end of the first year.  students are encouraged to explore broadly and have wide-ranging discussions with potential supervisors.  The Program Director must approve the selection of the primary supervisor and the co-supervisor.

Role and Responsibilities

The Supervisor is responsible for providing mentorship to the student through all phases of the PhD program. Thus; to the extent possible, the Supervisor will guide the selection of courses, dissertation topic, supervisory committee membership, and supervisory committee meetings; will assist with applications for funding; will make every effort to provide funding to the student directly; and will provide references for the student on a timely basis. The Supervisor also will comment on the student’s plan for preparation for the comprehensive examination. The Supervisor will guide the development of the student’s research proposal, and the implementation and conduct of all aspects of the research; advise on writing the dissertation; correct drafts and approve the final dissertation; and attend the defense.

Supervisory Committee

With the assistance of the Supervisor, and with the approval of the Program Director, the student will assemble a Supervisory Committee within the first year of study.

The Supervisory Committee, chaired by the Supervisor, will contribute advice regarding course selection; preparation for the comprehensive examination; selection of the dissertation topic; preparation and defense of the proposal; and implementation of the research plan. The Supervisory Committee also will provide timely and constructive criticism and guidance regarding data analysis, writing the dissertation, and preparing for its defense.

Composition

The Supervisory Committee generally will comprise the Supervisor and at least two members who hold either Full or Associate appointments in the SGS and may or may not hold a primary appointment in Epidemiology. Between these individuals and the Supervisor, there should be expertise in all content and methodological areas relevant to the student’s research focus and dissertation proposal. At times, when the student’s Supervisory Committee extends beyond the requisite Supervisor plus two SGS-qualified members, additional members may not necessarily hold SGS appointments (e.g., community members).  Non-SGS members, however, may participate only as non-voting qualified observers at the SGS Final Oral Examination (i.e., observer who has been approved by the student, the Supervisor, and the SGS Vice-Dean, Programs).

Supervisory Committee meetings will be held at least every six (6) months throughout the student’s PhD program. Under certain circumstances (e.g., during times of very rapid progress), the student and the Supervisory Committee may decide there is a need for more frequent meetings.

At the end of every meeting of the Supervisory Committee, the student and the Committee will complete the Supervisory Committee Meeting Report . All present must sign the report, which will be delivered to the Program Director and filed in the student’s progress file in the Graduate Department of Public Health Sciences.

The Report of the Graduate Department of Public Health Sciences Oral Defense Committee Meeting will be completed at the end of the Departmental Defense during which the Oral Defense Committee makes the recommendation for the student to proceed to the SGS Final Oral Examination (FOE).  The Report will also be signed and delivered to the Program Director and filed in the student’s progress file in the Graduate Department of Public Health Sciences.

Progress Through the PhD

The phases of the PhD program are identified by a set of accomplishments which the student generally will attain in order, and within a satisfactory time. These phases, which will be monitored by the Program Director of the PhD program, are the identification of the Supervisor and the Supervisory Committee, completion of required and elective course work, completion of the comprehensive examination, defense of the research proposal, and defense of the dissertation (both Departmental and SGS ). Full-time students are expected to complete the PhD within four (4) years. Flex-time students may take longer, but not more than eight (8) years; they must submit a revised list of milestones, for approval by the Supervisor and the Program Director.  Click here to view the PhD Epidemiology Timeline .

Research Ethics Board Approval

All research projects in which University of Toronto students are involved at any stage must have approval from the University of Toronto Research Ethics Board (REB). This includes ongoing research projects of the Supervisor which has previously received REB approval and where REB approval is already held from a University affiliated hospital or research institute. Preliminary work necessary to prepare the proposal may also require an original REB application or amendment to the original study. 
See details of the REB application and review process at Office of Research Ethics ( www.research.utoronto.ca/for-researchers-administrators/ethics/ ).

The dissertation proposal, as approved by the Program Director, must have University of Toronto Research Ethics Board approval as a supervised research study. An application for initial REB approval (or amendment to approval for an ongoing study), will therefore follow the approval of the dissertation proposal.

Dissertation

A dissertation in epidemiology must have relevance to the health of human populations. Within that broad framework, the dissertation may deal with any topic in the areas of medicine, public health and, health care services; and the research designs and statistical methods used in these fields. A doctoral dissertation in epidemiology may involve new data, collected for the purpose of the study, or the use of data previously collected. In the latter case, the analysis must be suitably complex, and must be driven by theoretical considerations and a specific research or methodological question. The dissertation result should be new knowledge and should include findings suitable for publication in peer-reviewed epidemiology journals. It may include both methodological and substantive advances in knowledge.

The dissertation topic must include clearly posed research questions amenable to study by appropriate epidemiologic methods. The student must have contributed substantially to the identification of the research question and must have played an integral part in the planning of the investigation. Wherever appropriate, the student will also be expected to participate directly in the collection of the data. Students will be expected to analyze their own data using appropriate analytic approaches.

Format Options for Dissertation

Students may choose one of two options for preparation of the dissertation: a monograph or a series of journal articles. The monograph is the default option. It is a single report, divided into chapters: introduction, literature review, methods, results, and discussion. A reference list would be followed by various appended material, which might include data collection instruments, additional related findings, and the like.

The journal article option varies from the monograph in that the main body of the dissertation comprises approximately three (3) complete, stand-alone manuscripts; these may already have been published, or may be ready to submit for peer-review. The manuscripts should be preceded and followed by material that unites them. So, for instance, an introduction and literature review, and possibly methods, more global in scope than those included in the manuscripts themselves, would precede the manuscripts; likewise, a discussion would follow, and would tie the manuscripts together, describing how they – as a group – make a contribution to the literature. Appended material might include the methodological details that would not be present in the methods sections of the manuscripts.

Regardless of format, the student should identify and follow appropriate style guides for the preparation of the dissertation.

Dissertation Defense

The student should aim to defend the dissertation within four years of entry into the PhD program. The defense of the dissertation will take place in two stages: first, a Departmental defense, second, a formal defense (the Final Oral Examination) before a University committee according to procedures established by the School of Graduate Studies (SGS). The two defenses generally are separated by about eight weeks.

Departmental Defense

The Departmental defense will be held after the completed dissertation has been approved by all members of the student’s Supervisory Committee, and the completion of the final Supervisory Committee meeting report. The purpose of this defense is to rehearse the oral presentation for the SGS defense and to determine whether the student is ready for the SGS defense.

The student should expect constructive criticism about the clarity and length of the presentation and the quality of visual materials, as well as about the dissertation itself. In particular, the Departmental defense will confirm that:

• The student has adequately met the requirements for a dissertation; and,
• The student has the required level of understanding of the scientific issues involved in the dissertation work.

The Departmental defense is attended by the student, the Supervisor and other members of the Supervisory Committee, and two reviewers with full SGS appointments. At least one reviewer should have supervisory experience in epidemiology at the doctoral level. The second reviewer may be a substantive expert from another discipline. Eligible reviewers will have had no prior involvement with the design or conduct of the research, with the exception of providing references or other background material, and generally will not be the faculty who served as reviewers at the proposal defense. The presentation will be advertised within the Graduate Department of Public Health Sciences, and other students and faculty are encouraged to attend.

• The Supervisory Committee approves the dissertation, at least four (4) weeks before the anticipated date of the defense.
• The Supervisory Committee identifies at least two potential reviewers.
• The student contacts the Program Director (copy to the Administrative Assistant) to give notice that the dissertation is ready for defense, together with the names and email addresses of potential reviewers. If necessary, the Program Director suggests alternative reviewers. The Program Director approves the reviewers, and will nominate one of them to be the Program Director’s representative.
• The Supervisor contacts reviewers and arranges the date/time of the defense, and informs the Administrative Assistant of the arrangements.
• The Administrative Assistant reserves a room and any required audiovisual equipment, as specified by the student, and posts notices on bulletin boards and e-mail, including a confirmatory e-mail to the Supervisory Committee and reviewers.
• The student distributes a copy of the dissertation to reviewers and to Supervisory Committee members four (4) weeks before the date of the defense, with an extra copy to the Supervisor (or designate) which may be made available to other faculty or students who may wish to read it.
• The Oral Defense Committee comprises the external reviewers, the Supervisor and the other Supervisory Committee members.
• Before the Oral Defense Committee convenes, the student and non-committee attendees may be asked to leave the room to permit discussion of the defense process among the Oral Defense Committee members.
• The defense will begin with a 20-minute presentation by the student of the research findings, followed by a period of questions and discussion among those present, with the two reviewers taking the lead in the questions. The Supervisor will chair the proceedings and act as timekeeper. The question period will typically be expected to last 60 to 80 minutes. The Supervisor will take notes of all issues raised.
• At the end of formal questioning, the student and other attendees will generally be asked to leave the room, and the Oral Defense Committee will discuss any issues of concern, to provide focused, constructive, and detailed feedback to the student, Supervisor, and other members of the Supervisory Committee on the dissertation and its oral defense. The Program Director’s Representative will take note of the feedback with respect to whether the dissertation work is generally adequate for the Final Oral Examination (FOE); changes that should be made to the dissertation prior to arranging for the FOE, and improvements that could be made to the oral presentation and defense; and will prepare a summary of the recommendations. If revisions to the text of the dissertation are recommended, there will also be discussion of the timing of the FOE. The student may be invited to be present at these discussions at the discretion of the Oral Defense Committee.
• At the end of the Departmental Defense, the Oral Defense Committee  will complete the Report of the Graduate Department of Public Health Sciences Oral Defense Committee Meeting. The options for proceedings are:

a) Dissertation is acceptable: ____    as is ____    with corrections/modifications as described in report to be prepared by the Program Director’s Representative

b) Another Supervisory Committee meeting required to see final dissertation: ____ Yes ____ No

c) If no, Committee member to see that changes are made: __________________________

d) Dissertation recommended for examination in: ______ months.

The Report will be delivered to the Program Director and filed in the student’s file in the Graduate office of Public Health Sciences.

School of Graduate Studies Final Oral Examination (FOE)

• Click here to view Policies & Procedures, PhD
• Click here to view the Procedures for Arranging PhD Defences

Current Student Profiles

		: Infectious disease epidemiology, sexually transmitted infections, HPV, HPV-related cancers, HIV, sexual health
		: Infectious disease epidemiology, genetic epidemiology “Genetic variants associated with new onset autoimmune disease following SARS-CoV-2 infection”
		: Communicable disease epidemiology, HIV/AIDS “The impact of the COVID-19 pandemic on healthcare engagement among People Living with HIV in Ontario”
		: Indigenous health, Indigenous research methodologies, substance use, homelessness “Using Indigenous worldviews and understandings of homelessness to develop and validate a new population-level assessment tool that measures chronic and episodic homelessness among First Nations, Inuit and Metis living in Toronto, Ontario”
		: Perinatal epidemiology, environmental epidemiology, social determinants of health, predictive modelling, epidemiologic methods  “Predicting and Preventing Adverse Pregnancy Outcomes in Canada”
		: Infectious disease epidemiology, Cancer epidemiology, Cancer survival and prognosis “An examination of the impact of infection on survival and prognosis in cancer populations”
		Cardiovascular epidemiology, sports medicine, mental health, cardiac arrest, health services research, social determinants of health
		: Machine Learning, Artificial Intelligence, Predictive Modelling, Imaging and Big Data.  “High-Dimensional Analysis to Pinpoint the Origin of Pain Among Postmenopausal Women with Knee Osteoarthritis Using Convolved Features from Knee MRI Scans”
		: Women’s health, reproductive health, chronic disease, aging, lifecourse epidemiology “Reproductive health and chronic disease across the lifecourse among postmenopausal women”
	and	: Infectious disease modelling, emerging infectious diseases, mpox, real-world vaccine effectiveness “Limiting biases in measures of vaccine effectiveness from real-world data during the evolving mpox outbreak in Canada and Internationally”
		: Chronic disease epidemiology, women’s health, children’s and adolescent’s health, social determinants of health, knowledge synthesis “Impact of migraine and migraine-related comorbidity on perinatal outcomes”
		: Emerging infectious diseases, HIV/STI epidemiology, community-based participatory research, mathematical modelling, global health security “Community-based participatory modeling of HIV transmission: Assessing the influence of sexual networks on HIV epidemics among men who have sex with men in Kenya”
	and	: COVID-19, pediatric epidemiology, machine learning, predictive modelling, pediatric interventions
		: Intersection of social demographic factors and infectious disease epidemiology. The Unequal Landscape of COVID-19 in Toronto
		: Global mental health, psychiatric epidemiology, excess mortality due to suicide
	and	Chronic disease epidemiology, population health intervention research, social epidemiology, health equity, public health policy “The alcohol-harm paradox and health equity impacts of alcohol policy in Canada: Evidence to inform the complex relationships across alcohol policy, consumption, and harms” (Working title)
		: Global health, HIV/AIDS, implementation science, social epidemiology, housing and homelessness
	&	: Infectious disease epidemiology, vaccine-preventable diseases, global health “Waning measles immunity in Ontario: A population-based cohort study”
	and	Measuring the burden of respiratory syncytial virus among older adults living in Ontario Infectious diseases; vaccine policy, effectiveness, and communication; health equity research
	&	: Environmental toxicants, neurocognitive development, fetal exposures, child health, global health “The role of environmental toxicant exposure on neurodevelopment in children: examining cognitive and behavioural symptoms among mother-child pairs from two environmental birth cohort studies.”
		: Mental health, sexuality, health services research, predictive modelling, machine learning, psychometric evaluation
	and	: Infectious Disease Epidemiology, Spatial Epidemiology, Artificial Intelligence, Predictive Analytics
		: Health services research, remote patient monitoring, population health, modifiable risk factors, molecular epidemiology, machine learning
		: Polypharmacy, pharmacoepidemiology, health administrative data
		: Infectious disease epidemiology, HIV, sexually transmitted infections, sexual health research, community-based research
		: Social conditions and health, methods for population-based health research, life course epidemiology, chronic disease epidemiology “Addressing the single-risk factor framework through deep learning methods: applications in multimorbidity”
		: Infectious disease epidemiology, hepatitis C, HIV, and other sexually transmitted and blood-borne infections, harm reduction, and health disparities research “Measuring uptake and effectiveness of direct-acting antiviral treatment for hepatitis C among key populations in Ontario: a population-based retrospective cohort study.”
	&	Understanding the mechanisms of how estradiol loss at menopause leads to knee pain: A population-based longitudinal study of postmenopausal women Chronic disease epidemiology, causal inference, aging, and women’s health
Ijeoma Itanyi	and	Non-communicable disease epidemiology, Multimorbidity, Population health, Electronic Medical Records, Machine learning
		: Cancer epidemiology, biomarkers, pharmacoepidemiology “Improving the safety and efficacy of treatment for metastatic colorectal cancer by understanding the genetic influences on the mechanism of action of the epidermal growth factor receptor targeting monoclonal antibody drug cetuximab using data from the Canadian Cancer Trials Group CO.17 and CO.20 randomized controlled trials.”
	&	: Maternal and child health, global health, methodology – observational cohort studies, infectious disease epidemiology Measurement of breastfeeding practices and infant intake of breast milk components in epidemiological research
		: Indigenous health, Indigenous research methodologies “Using an Indigenous theoretical framework to measure Indigenous Homelessness and its’ impacts of Indigeneity and substance use among Indigenous Peoples living in urban and related homelands.”
		“The burden of cancer among people living with HIV in Ontario and the effect of immune function and engagement in HIV care on cancer risk.”
		chronic disease epidemiology, disability studies, child health, health services research
		: Mental health, social epidemiology, occupational health, machine learning “Using unsupervised machine learning methods to identify service use patterns and gendered care pathways in the publicly funded mental healthcare system in Ontario.”
		: Antimicrobial resistance, antimicrobial utilization, big data
		: Prediction Modelling, Machine Learning, Environmental Health, Health Services Research, Premature Mortality “Developing Population-Based Risk Tools to Predict and Reduce Premature Mortality in Canadian Cities.”
		: Environmental epidemiology, neurologic outcomes, methods & app data “Estimating associations between air pollution and migraine using smartphone app data”
		Infectious disease epidemiology, mathematical modelling, substance use epidemiology “Leveraging population-based modelling approaches to inform respiratory disease prevention”
		: Population health, emergency medicine, health services
		: Social epidemiology, mental health for racialized populations, health equity, mixed methods methodology, evaluation
Twitter:	and	Infectious disease surveillance using emerging data sources: Applications to antimicrobial resistance and COVID-19 Infectious disease epidemiology, COVID-19, antimicrobial resistance, antimicrobial stewardship, infectious disease surveillance
		Rare disease, knowledge translation, evidence synthesis, patient-engagement in research
	and Alyson Mahar	: Veteran and military mental health; psychiatric epidemiology; social epidemiology “Sex-specific differences in mental health service utilization amongst Canadian Armed Forces Veterans: a population-based study.”
Twitter:		Participant-owned wearables for evaluating longitudinal trends in physical activity during the COVID-19 pandemic. Participate in this research by downloading the . More information here: Behavioural epidemiology, mobile health data, physical activity
	and	Pharmacoepidemiology, perinatal epidemiology, pediatric health, global health, women’s health “Examining the association between prenatal antidepressant exposure and maternal and child/adolescent cardiometabolic outcomes”
		Longitudinal approaches to the epidemiology of total knee arthroplasty: Trends, determinants, and postoperative outcomes Arthritis, musculoskeletal health, chronic disease epidemiology, clinical epidemiology, social determinants of health,  correlated/longitudinal data analysis, complex survey and health administrative data analysis, causal inference from observational data.
		: health services research, health technology assessment, healthcare access
		: Social epidemiology, population health, premature mortality, predictive modeling, machine learning “Understanding, predicting, and preventing mortality from deaths of despair: a population-based approach to addressing stagnating life expectancy in Canada.”

Alumni Profiles

Isha Berry		“Transmission dynamics of influenza and avian influenza in urban Bangladesh: live poultry exposure, seasonality, and pandemic risk at the human-poultry interface” Infectious disease epidemiology, global health, mathematical modelling, one health, emerging infectious diseases, influenzas
	&	: Maternal and infant health, Maternal illicit drug use, child health, health equity, public health policy Title: “Health and Developmental Outcomes Associated with Prenatal Opioid Exposure: A Population-based Retrospective Cohort Study in Ontario.”
Twitter: @EpiHarris		“Antipsychotic reduction efforts in long-term care: Examining the extent and potential impact of medication substitution.” Pharmacoepidemiology, neurodegenerative diseases, aging, artificial intelligence and data science
		I : nfectious diseases, pertussis, immunization research, public health policy, and applied machine-learning “The problem with pertussis: Finding uncaptured pertussis cases in the Electronic Medical Record Primary Care (EMRPC) to improve estimates of burden and vaccine effectiveness.”
		: Cannabis legalization, alcohol policy interventions, addiction and mental health, Indigenous health
		: Molecular and genetic epidemiology, cancer epidemiology, risk-prediction, cancer prevention and early detection methods, machine learning, deep learning, Bayesian methods
		: Chronic disease epidemiology, health services research, musculoskeletal health, clinical epidemiology, knowledge synthesis “Examining the effects of low back pain and mental health symptoms on health care utilization and costs.”

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Institute for Health Informatics

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Health Informatics PhD Graduate Program

The PhD program is designed for students seeking the highest level of advanced training in the area of health informatics.

Students take a sequence of core courses in health informatics, computing, and biostatistics, and electives in technical and health science areas, and pursue one of four tracks: Data Science and Informatics for Learning Health Systems ; Clinical Informatics ; Translational Bioinformatics ; or Precision and Personalized Medicine (PPM) Informatics . Students pursuing the Data Science and Informatics for Learning Health Systems track are expected to complete the University’s Data Science MS degree en route to the PhD.

• Stan Finkelstein

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The Role of Data Science in Healthcare

The integration of data science into healthcare dates back to the 1960s when computers began managing patient data, marking the start of medical data utilization. In 1965, the National Library of Medicine launched MEDLINE, one of the first biomedical literature databases, setting the stage for modern medical informatics. 1

Today, data science is revolutionizing patient care through predictive analytics, personalized treatments, and streamlined operations. By applying Big Data strategies, the U.S. healthcare system could potentially generate up to $100 billion annually by optimizing clinical operations, reducing costs, and improving patient outcomes. 2

One of the key benefits of data science is its ability to provide actionable insights that enhance patient outcomes. Analyzing vast healthcare data uncovers patterns and trends that enable early disease detection, treatment optimization, and efficient resource management.

We’ll explore the role of data science in healthcare, its significance, various applications , and the importance of healthcare data scientists. Additionally, we'll discuss how you can leverage data science to make a significant impact in the healthcare industry.

What Is Data Science in Healthcare?

Data science in healthcare involves applying advanced analytical techniques to healthcare data to extract meaningful insights. This interdisciplinary field combines statistics, machine learning, and Big Data technologies to analyze and interpret complex medical data.

At its core, data science is about discovering patterns and making predictions from large data sets. In healthcare, this translates to predicting patient outcomes, personalizing treatment plans, and optimizing resource allocation. Data science is crucial for early detection of chronic diseases, optimizing healthcare spending, and enhancing patient experiences through personalized care.

Traditional data analysis often focuses on descriptive statistics to summarize historical data, while data science goes further by leveraging machine learning and AI for predictive power. For example, predictive models can forecast disease outbreaks, identify at-risk patients before symptoms appear, and recommend personalized treatments based on genetic profiles.

Predictive analytics is a common application of data science in healthcare, using current and historical data to make real-time predictions. For instance, predictive models have significantly reduced mortality rates by identifying patients at risk for sepsis. 3

By combining massive datasets with sophisticated algorithms, data science empowers healthcare professionals to make better, data-driven decisions.

Why Data Science Matters in Healthcare

Data science holds immense potential to transform the healthcare sector, impacting everything from patient care to healthcare system efficiency. By leveraging vast amounts of data, healthcare providers can make informed decisions that enhance patient outcomes and streamline operations.

By adopting data science methodologies, healthcare institutions not only improve patient care but also make their operations more cost-effective and efficient. The integration of predictive analytics and machine learning into healthcare practices signifies a shift towards more proactive, personalized, and efficient healthcare delivery.

How Is Data Science Used in Healthcare?

Data science is applied in innovative ways to improve patient care and optimize healthcare operations. Healthcare organizations are working towards modernizing their data systems and protecting patient information, leveraging data science and technology to enhance patient care and operational efficiency.

Below are some key applications:

Predictive Analytics

One of the most significant impacts of data science in healthcare is its ability to improve patient outcomes. Predictive models can analyze patient data to identify individuals at high risk for diseases, allowing for early interventions. For instance, machine learning algorithms can more accurately predict hospital readmissions than traditional methods, reducing readmission rates and enhancing patient care. 4

Predictive analytics, which uses historical data to forecast future events, is central to this process. Effective data management ensures the quality and security of patient data, which is essential for accurate predictions. In healthcare, predictive analytics can anticipate patient outcomes, identify those at risk for diseases, and recommend preventive measures. Research has shown that predictive models can accurately forecast the onset of conditions like diabetes and heart disease, enabling timely interventions and reducing complications. 5

Personalization of Treatment

Personalized medicine uses patient-specific data to create tailored treatment plans. By analyzing genetic information, lifestyle factors, environmental influences, and medical images, healthcare providers can develop customized protocols that are more effective. This approach has been particularly successful in oncology, where personalized treatments have significantly improved patient outcomes and survival rates.

Data science enhances this personalization by enabling deeper analysis of genetic, environmental, and lifestyle data. For example, oncologists can use predictive analytics to identify the most effective chemotherapy protocols for individual cancer patients, optimizing treatment efficacy while minimizing adverse effects.

Disease Outbreak Prediction

Data science plays a critical role in predicting disease outbreaks, which is essential for public health planning and response. Machine learning algorithms analyze diverse datasets, including social media trends, travel patterns, and epidemiological data, to forecast potential outbreaks. The Centers for Disease Control and Prevention (CDC) utilized Big Data analytics to predict and manage the Zika virus outbreak in 2016, which significantly improved containment efforts. 6

Operational Efficiency

Operational efficiency is crucial for healthcare institutions striving to deliver high-quality care in a cost-effective manner. Data science plays a key role in optimizing various operational aspects, such as staffing, supply chain management, and patient flow. For example, predictive analytics can forecast patient admission rates, enabling hospitals to allocate resources more efficiently. By using data-driven staffing solutions, hospitals can reduce labor costs while maintaining high standards of care. 7

Data science also transforms healthcare systems by enhancing overall operational efficiencies. Hospitals can leverage data analytics to manage staffing levels, minimize wait times, and optimize resource allocation. A report from the National Institutes of Health highlighted that implementing AI-driven scheduling systems led to a 15% increase in patient throughput and a 12% reduction in operational costs. 8

The Role of Healthcare Data Scientists

Healthcare data scientists play a crucial role in harnessing the power of data to improve patient outcomes, streamline operations, and advance medical research. Their responsibilities and skills are diverse, encompassing data collection, analysis, and interpretation. In the field of health data science, the average salary for professionals can vary significantly based on location. Entry-level positions may start at a lower range, while experienced professionals can command higher salaries. Conducting localized salary research is essential to understand the specific figures, with sources like Glassdoor providing valuable insights.

Key Responsibilities:

• Data Collection and Cleaning: Healthcare data scientists gather data from various sources, including electronic health records (EHRs), wearable devices, and clinical trials. They ensure the data is clean, accurate, and ready for analysis.
• Data Analysis and Interpretation: Using statistical methods and machine learning algorithms, data scientists analyze the data to uncover patterns and insights. This analysis helps in predicting patient outcomes, personalizing treatments, and identifying disease trends.
• Algorithm Development: They develop and implement machine learning models and algorithms tailored to specific healthcare needs, such as predicting patient readmissions or identifying potential outbreaks.
• Collaboration with Medical Professionals: Data scientists work closely with doctors, nurses, and other healthcare professionals to ensure that the data insights translate into actionable medical decisions. Collaboration is key to making data-driven healthcare a reality.

Skills Required:

• Statistical and Analytical Skills: A strong foundation in statistics and data analysis is essential for interpreting complex healthcare data.
• Programming Skills: Proficiency in programming languages like Python, R, and SQL is necessary for data manipulation and analysis
• Knowledge of Machine Learning: Understanding machine learning techniques and their application in healthcare is crucial for developing predictive models.
• Domain Knowledge: Familiarity with medical terminologies, healthcare regulations, and ethical considerations is important for applying data science effectively in the healthcare context.
• Communication Skills: The ability to convey data insights to non-technical stakeholders, including healthcare professionals and policymakers, is vital for driving change.

The role of a healthcare data scientist is dynamic and interdisciplinary. The demand for data scientists in healthcare is expected to grow by 35% by 2032, reflecting the increasing reliance on data-driven decision-making in the industry. 9

Transform Healthcare with Your Data Science Expertise

As healthcare continues to evolve, the integration of data science has proven to be a game-changer, significantly enhancing patient care and operational efficiency. With the increasing complexity of healthcare challenges, the demand for skilled data scientists who can navigate and interpret vast amounts of data has never been higher.

By mastering data science , you can play a pivotal role in advancing healthcare. Whether it's through developing predictive models that save lives, personalizing treatment plans, or optimizing hospital operations, your expertise can have a profound impact on the industry.

If you’re inspired to make a difference, consider advancing your education with a focus on healthcare data science. Many universities now offer specialized programs that equip you with the necessary skills and knowledge to excel in this field. These programs typically cover essential topics such as machine learning, statistical analysis, and healthcare informatics, providing a comprehensive foundation for your career.

Explore New York Institute of Technology’s Online Data Science, M.S. program to gain cutting-edge skills and join the ranks of professionals making significant impacts in healthcare. Our program offers a flexible online learning environment, personalized mentorship, and access to a network of industry experts.

For more information on admissions, course offerings, and career support, visit our admissions page or contact our admissions outreach advisors. Transform healthcare with your data science expertise and be at the forefront of innovation in this vital industry.

• Retrieved on August 14, 2024 https://pubmed.ncbi.nlm.nih.gov/20485673/
• Retrieved on August 14, 2024 https://www.mckinsey.com/industries/healthcare/our-insights/the-big-data-revolution-in-us-health-care
• Retrieved on August 14, 2024 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317482/
• Retrieved on August 14, 2024 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8101040/
• Retrieved on August 14, 2024 https://www.nature.com/articles/s41586-019-1797-8
• Retrieved on August 14, 2024 https://www.cdc.gov/mmwr/volumes/66/wr/mm6609a3.htm
• Retrieved on August 14, 2024 https://www.shiftmed.com/blog/the-benefits-of-ai-powered-demand-forecasting/
• Retrieved on August 14, 2024 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083221/
• Retrieved on August 14, 2024 https://www.bls.gov/ooh/math/data-scientists.htm#:~:text=Employment%20of%20data%20scientists%20is,on%20average%2C%20over%20the%20decade.

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