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 in Python/R for data analysis.
Prerequisites: Prior coursework in biology and probability.
Offered SPR, 4.0 credits, Offered every year

GENOME 559 – Introduction to Statistical and Computational Genomics

Emphasis on basic probability and statistics, and in introduction to computer programming. This course is intended to introduce students with non-computer science background to the major concepts of programming and statistics. After taking this course, students will be able to describe and perform basic analysis tasks relating to biological sequence analysis, phylogenetics, pedigree analysis, genetic association studies, population genetics and microarray analysis. Students will be able to demonstrate an understanding of fundamental statistical concepts, such as p-values, t-tests, chi-squared tests and multiple testing correction. Finally, students will be able to write computer programs to perform statistical and bioinformatics analysis.
Offered WIN, 3.0 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, Weeks 1-10, 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. This course is best suited for students with minimal prior experience in programming or computational biology but interested in learning the best practices.
Offered AUT, 3.0 credits, 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 year

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

GENOME 569 – Bioinformatics Workflows for High-Throughput Sequencing Experiments

Programming skills and software tools for building automated bioinformatics pipelines and computational biology analyses. Emphasis on UNIX tools and R libraries for distilling raw sequencing data into interpretable results. For students familiar with UNIX and with some programming experience in Python, R, or C/C++.
Offered SPR, 1.5 credits, Weeks 1-5, 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

The following courses ae offered through the Information School. Some courses may be offered irregularly. If a course looks interesting to you, check with the department to see when it will be offered next. Additional course fee required for Informatics courses.

IMT 511 – Introduction to Programming for Information and Data Science

Introduces fundamentals of computer programming as used for data science. Covers foundational skills necessary for writing stand-alone computer scripts, including programming syntax, data structuring, and procedural definition (functions). Includes programming tools and environments (e.g., command-line). Emphasizes skills in language syntax, debugging, algorithmic thinking, and data comprehension. Assumes no previous programming background.

IMT 561 – Visualization Design

Students develop a human-centered visualization design practice using real-world data. This process includes applying graphic principles of visual encoding to data; conducting design explorations using sketches and prototyping; and gathering user feedback to assess output. Design workshops provide opportunities for hands-on engagement with concepts and technical skills.

IMT 562 – Interactive Information Visualization

Introduces techniques for visualizing, analyzing, and supporting interaction with structured data numbers, text, graphs). Provides experience creating interactive visualizations for the web. Exposes students to cognitive science, statistics, and perceptual psychology principles. Students design and evaluate visualizations using perceptual and statistical accuracy.

IMT 572 – Introduction to Data Science

Introduces a broad, non-technical overview of key concepts, skills, and technologies used in “data science”. Provides a high-level introduction to common data science pipelines, such as experimental design, data collection and storage, basic analytics, machine learning, and data visualization, focusing on analyzing in real-world datasets using industry standard statistical packages.

IMT 573 – Data Science I: Theoretical Foundations

Introduces technically focused theoretical foundations of “Data Science.” Provides an overview of key concepts, focusing on foundational concepts such as exploratory data analysis and statistical inference. Assignments are data-intensive and require significant programming and statistical analysis. Students are expected to have college-level statistics and programming experience (R and python preferred).
Prerequisite(s): either QMETH 201, IMT 570, or equivalent college coursework; and either CSE 121, CSE 122, CSE 123, CSE 142, or equivalent college coursework.

IMT 574 – Data Science II: Machine Learning

Provides theoretical and practical introduction to modern techniques for the analysis of large-scale, heterogeneous data. Covers key concepts in inferential statistics, supervised and unsupervised machine learning, and network analysis. Students learn functional, procedural, and statistical programming techniques for working with real-world data.
Prerequisite(s): IMT 573.