Clinical Bioinformatics

Bioinformatics is an interdisciplinary field focused on analyzing biological data with computational tools and methods to extract insights and infer meaning from complex datasets. To meet this rapid development, this program is firmly grounded in theory, hands-on skills and application. In the Clinical Bioinformatics graduate certificate, students gain insight into the genetic underpinnings of disease, contributing to the broader landscape of biomedical discovery and innovation through the application of computational analysis techniques.   

Students are introduced to the rapidly evolving fields of -omics research with a focus on genomics, transcriptomics and pharmacogenomics concepts pertaining to human health and disease, providing students with training in analytical approaches used in these areas. Students acquire expertise navigating genetic databases and developing data analysis pipelines using current bioinformatics software to uncover insights into the pathology of disease and aid in developing predictive models. There is also an emphasis on effective data mining and scientific reproducibility, highlighting best practices for the integration and management of data across various platforms using cloud computing. 

This program prepares graduates with the skills needed to build successful careers supporting -omics research across many areas in the health-care sector such as in health care and government institutions, pharmaceutical companies, and in academic research labs. The knowledge of programming languages and competencies in machine learning that are covered in our curriculum provides learners with highly valuable transferrable skills. Graduates of this program cultivate a versatile skillset that extends beyond bioinformatics, opening doors to opportunities in the broader realms of data science and technology. 

This program is offered in two formats,

1. Online Synchronous - open to domestic students only
2. Hybrid with in-person and online courses - open to both domestic and international students

As part of your program, you will participate in one mandatory paid or unpaid 210-hour field experience to gain professional work experience in the field of clinical bioinformatics. The field experience takes place during the second semester and can be extended into the summer as needed. Work can occur in diverse settings such as for-profit or not-for-profit enterprises, governmental or educational institutions, or health and wellness institutions.

The experience will enhance the theoretical learning in the program, giving you hands-on application in clinical bioinformatics. Field experiences might include data curation, developing analysis pipelines, building machine learning models, and contributing to writing publications as part of a bioinformatics research group or core facility. It can also involve research and development roles in private companies in the medical genetics and precision health space. Students are supported throughout their field experience with various key supports and key faculty and staff from the program. 

While Humber assists students find their field experience by working with industry partners and identifying openings, students are responsible for finding their own field experience aligned with the program's learning outcomes. 

Visit Future Students > Work-Integrated Learning > to learn more about your WIL power and other success stories.

Also take time to explore the digital Faculty of Health Sciences & Wellness Student Handbook to learn foundational WIL concepts, plus requirements specific to this program.

Given the rapid development in both biological and data sciences, and as Big Data in the field of medicine continues to grow at an accelerated rate, demand is growing for highly skilled bioinformatics professionals with life science education background. Graduates of this interdisciplinary program will be versatile and demonstrate the core competencies to pursue a wide variety of careers such as: 

• bioinformatics analyst, bioinformatician 
• clinical research project assistant/co-ordinator 
• computational genomicist 
• computational molecular scientist 
• data scientist 
• drug discovery researcher 
• research and development specialist