Health data science is a rapidly growing research field across academia, government, and industry. It relates to the application of statistical and machine learning approaches to analyse large complex medical datasets including electronic medical records, radiological imaging, physiological sensor data, and longitudinal databases. This course combines an overview of these key types of medical data, hands-on introduction to their principal analysis methods, and training in how to apply them in interdisciplinary research contexts. Using a combination of lectures, R-based exercises, student-driven tutorials, and collaborative development of a research proposal, students will gain the skills necessary to plan and conduct effective health data science research.
This course is offered jointly to the graduate students in Community Health & Epidemiology and the Faculty of Computer Science. It is also open to advanced undergraduates with prior experience with statistics (e.g., CSCI2110 and either STAT2060 or CSCI2360)
This course focuses on teaching the underlying algorithms behind commonly used computational and statistical approaches for analysing biological data. The majority of material focuses on biological sequence data and includes modules on homology (comparing sequences), assembly (recovering genomes from sequencing data), phylogenetics (inferring evolutionary relationships), and machine learning (applying ML to biological data). Students get hands-on experience via 4 practical assignments and given experience reading and critiquing active research literature via a capstone assignment.
This course was originally created by Robert Beiko It is a graduate course for CS or life sciences (or similar) students that is open to advanced undergraduates as a final year elective.
This is a core course offered throughout the 1st year of medical school at Dalhousie. Professional competencies includes a weekly two-hour tutorial followed by a one-hour large group session. This unit provides medical students with the opportunity to integrate their biomedical and clinical learning within the context of patient care from a professional, community and life-long learner perspective.
Content includes public health and infectious disease management in the community, end-of-life decision-making and other ethical challenges, patient safety and other system and quality improvement approaches, social accountability and global health, physician wellness and career paths, and the Health Mentors Program.
Key concepts come from population health, epidemiology, ethics, law, informatics, health policy and the humanities. The unit is highly applied and case-based, and closely integrated with the other Med 1 units through shared cases and topics.
Originally developed by Thomas Trappenberg and is an undergraduate course offered within the Faculty of Computer Science.
This course is an introduction to probability theory and statistics with applications to computer science, in particular to data science and experimental CS. Students will be introduced to the idea of random numbers and the formal language of reasoning with uncertainty as well as with the basic tools and an outlook of advanced tool for experimental investigations in computer science such as HCI. What is a random number, probability mass and density functions, data analytics, probabilistic reasoning, basic hypothesis testing, sample size estimation, and inter-rater reliability. Applications and relevance of these concepts in computer science will be emphasized.
This course is still under developement but will be a broad introduction to the ways in which genomic data is used in medicine. This encompasses infectious disease genomic epidemiology and topics in human genomics (such as genome-wide assocation studies).
This course is offered jointly to the graduate students across the Faculty of Medicine (and listed within Community Health & Epidemiology) and the Faculty of Computer Science.
It will also be open to advanced undergraduates as a final year elective.
This is a workshop that provides multidisciplinary research training and hands-on mentorship for the development of pilot health research projects. The aim of this workshop is to bring together diverse individuals with limited prior research experience and help them develop the capacity to ask their own research questions. The initiative then provides funding to enact small/pilot projects emerging from these proposals.
More details about MicroResearch can be found here
This is a workshop as part of the Canadian Bioinformatics Workshop series It is a 3-day course that provides an introduction to genomic epidemiology analysis and hands-on practical tutorials demonstrating the use selected analysis tools. The tutorials are designed as self-contained units that include example data and detailed instructions for installation of all required bioinformatics tools or access to publicly available web applications.
Participants gain practical experience and skills to be able to:
Understand next generation sequencing (NGS) platforms as applied to pathogen genomics and metagenomics sequencing
Analyze NGS data for pathogen surveillance and outbreak investigations
Analyze antimicrobial resistance genes
Detect emerging pathogens in metagenomics data
Perform phylogeographic analysis
Use different visualization tools for genomic epidemiology analysis
Course materials and recordings are freely available:
This was a joint initiative between CLIMB-BIG-DATA, Public Health Alliance for Genomic Epidemiology (PHA4GE) and Joint Programming Initiative on Antimicrobial Resistance (JPIAMR) workshop. This workshop was developed to provide high-level training in the genomics methods for the analysis of antimicrobial resistance. It was designed to help build AMR-genomics capacity for academic, clinical, and public health groups.
Course materials and recordings from the 2021-OCT-15 offering are available freely online here