COURSE CURRICULUM

The Bioinformatics degree program at Langara College requires a total of 129 credits and is structured to provide a comprehensive interdisciplinary education combining biology, chemistry, computer science, mathematics, and statistics. The lower-level portion comprises 80 credits and includes foundational coursework such as General Biology I and II, Biochemistry, Genetics, Cell Biology, General and Organic Chemistry, and introductory programming and computing courses including Algorithms, Object-Oriented Programming, and Database Systems. Students also complete introductory and intermediate statistics, linear algebra, and calculus courses tailored to life sciences. Alongside the academic curriculum, students engage in co-operative education through two mandatory work placements (COOP 2301 and COOP 2501), providing real-world industry experience. Additional components include Bioinformatics Industry Topics I and II, employment preparation, and six credits of English along with three to six credits of non-science electives to ensure a well-rounded academic foundation. At the upper level, students complete 49 credits, which focus on advanced bioinformatics applications and tools. Key courses include Molecular Genetics, Genomics, Transcriptomics, Proteomics, Metabolomics, Evolution and Phylogenetics, and Molecular Modeling, all critical for modern biological data analysis. Computing courses such as Data Transformations, Cloud and Parallel Computing, Data Mining, Machine Learning, and Data Visualization further equip students with computational skills. The curriculum is completed with Statistical Methods for Biological and Health Sciences and culminates in two capstone courses where students apply their learning in a practical project. Throughout the program, Bioinformatics Industry Topics III and IV ensure students remain connected to industry trends and practices. This integrated curriculum bridges theoretical knowledge with practical experience, preparing graduates for careers in biotechnology, health research, pharmaceuticals, and computational biology, or for further academic study in graduate programs. The program emphasizes not only technical proficiency but also the critical thinking and interdisciplinary collaboration needed to solve complex biological problems through data science.