Highlighted New and Updated Courses for 23W

Students » The Distillation Blog » Highlighted New and Updated Courses for 23W

There are some new additions and replacements for courses offered by the Faculty of Science. Read on to learn about these highlighted courses.

ATSC 413: Forest Fire Weather and Climate

Wildfire hazards are increasingly threatening people living near the urban/forest interface. These threats are exacerbated by climate change, population growth, and forest-fire-suppression practices. ATSC 413 is designed to educate highly qualified personnel with the technical skill set to forecast forest fire weather, to anticipate evolution of fire hazards associated with a changing climate, and to advise on sustainable fire-mitigation policies.

BIOL 303: Green Planet

The biology and history of plants have played a major role in the rise and fall of civilizations. From crop selection for food to the development of medicinal products, learn about the historical, economic, and social implications of plants and plant products. This multidisciplinary course also covers principles of plant physiology, wood processing, nutrition and agricultural practices.

BIOL 348: Biology of Cannabis

Cannabis has become increasingly integrated into mainstream cultural, social, health, and economic institutions. Given its social relevance, explore the biological aspects, impacts on the human endocannabinoid system, and human applications of cannabis through local examples and real-world situations.

CHEM 141: Chemical Bonding, Molecular Structure and Properties for Lab Sciences

Designed for those interested in pursuing a major in Chemistry, Biochemistry, Pharmacology, Forensic Sciences, and other lab-focused sciences, this course that is equivalent to CHEM 121 focuses on real-world applications. Learn technical lab skills on bonding theories, chemical structure, and observable properties. The labs are designed for those with no lab experience, but who are willing to learn.

EOSC 448: Anthropocene Earth

The Anthropocene is a proposed new geologic epoch in which humans have become the dominant driving force on Earth’s natural cycles and processes. The Anthropocene looms large over every aspect of study in both the humanities and in the physical environment. Because of the complex interactions between modern human society and their environments, this course may touch on some topics in the humanities or social sciences, but the focus will be on the scientific aspects of the Anthropocene in the natural environment. Some of the big questions to address are: when did the Anthropocene start? Why are humans so good at modifying environments? How do you define a new geologic epoch? How do you assess anthropogenic change? What is the impact of anthropogenic processes on the biosphere, lithosphere, hydrosphere and ecosphere? How does anthropogenic change intersect with research in Earth and Environmental Sciences? Are all aspects of anthropogenic change harmful to the environment?

MICB 211: Foundations in Microbiology (replaced MICB 201)

Learn about the physiology and genetics of bacteria, and the role of bacteria in the environment in the cycling of elements (carbon, nitrogen) and bioremediation. Discover the mode of action of several antibiotics, as well as how bacteria develop resistance to antibiotics.

MICB 212: Introductory Immunology and Virology (replaced MICB 202)

Focus on how the immune system responds to eliminate infections caused by bacteria and viruses; study how viruses replicate, cause disease, and evade the immune system. Explore how vaccines are developed in response to new pathogens.

PHYS 310: Machine Learning for Physics and Astronomy Data Analysis

Discover the application of Machine learning (ML) to observational and simulated data analysis. Learn the fundamental concepts of data-driven inference alongside traditional numerical analysis and differential equations. Topics include algorithms for data structuring, dimensionality reduction, linear regression and classification, artificial neural nets, convolutional neural nets, unsupervised learning.