Computational Biology

GENOME 541 – Introduction to Computational Molecular Biology: Molecular Evolution

Computational methods for studying molecular evolution. Students must be able to write computer programs for data analysis. Prior coursework in biology and probability are highly desirable.
Offered SPR, 4.0 credits, Offered every year

GENOME 560 – Introduction to Statistical Genomics

An introduction to fundamental concepts necessary for the analysis of genetic and genomic data including, basic elements of probability theory, parameter estimation, and hypothesis testing.
Offered SPR, 3.0 credits, Offered every year

MCB 536 – Tools for Computational Biology

Introduction to established best practices in computational biology. Learn to organize unstructured data into standard formats, transform data for statistical analyses, and visualize the transformed data. Learn workflows for reproducible research such as version control, project organization, and code documentation. Gain basic experience with Linux command line tools and the Python and R programming languages. Classes will involve hands-on learning through coding exercises, collaborative problem solving, and extensive use of online learning resources.
Offered AUT, 3.0 credits, Offered every year

BIOL 519 – Data Science for Biologists

Explores, analyzes, and visualizes biological data sets using scientific computing software. Focuses on the foundations of data wrangling, data analysis, and statistics, particularly the development of automated techniques that are reproducible and scalable to large data sets.
Offered WIN, 4.0 credits, Weeks 1-10, Offered every year

CSE 512 – Data Visualization

Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology and cognitive science. Topics: data and image models, visual encoding, graphical perception, color, animation, interaction techniques, graph layout, automated design. Lectures, reading and project.
Offered SPR, 4.0 credits, Weeks 1-10, Offered every other year (last offered in SPR 2022)

CSE 527 – Computational Biology

Introduces computational methods based on artificial intelligence (AI) and machine learning (ML) techniques for understanding biological systems and improving health care. AI/ML techniques such as explainable and interpretable ML, deep neural network learning, probabilistic graphical models, causal inference, and deep learning techniques are covered. Problem areas such as genetics, epigenomics, expression data analysis, proteomics, and electric health record data analysis are covered.
Offered AUT, 4.0 credits, Weeks 1-10, Offered every year

CSE 583 – Software Development for Data Scientists

Provides students outside of CSE with a practical knowledge of software development that is sufficient to do graduate work in their discipline. Modules include Python basics, software version control, software design, and using Python for machine learning and visualization.
Offered AUT, 4.0 credits, Weeks 1-10, Offered every year

GENOME 540 – Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis

Algorithmic and probabilistic methods for analysis of DNA and protein analysis. Students must be able to write computer programs for data analysis. Prior coursework in biology and probability is highly desirable.
Offered WIN, 4.0 credits, Weeks 1-10, Offered every year

STAT 509 – Econometrics I: Introduction to Mathematical Statistics

Examines methods, tools, and theory of mathematical statistics. Covers, probability densities, transformations, moment generating functions, conditional expectation. Bayesian analysis with conjugate priors, hypothesis tests, the Neyman-Pearson Lemma. Likelihood ratio tests, confidence intervals, maximum likelihood estimation, Central limit theorem, Slutsky Theorems, and the delta-method.
Offered AUT, 4.0 credits, Weeks 1-10, Offered every year