Special courses

Special courses

The following special courses pertaining to computational and data science that may be of interest to students both inside and outside of CMSE.  We anticipate that a significant number of additional courses will be added each year - check the course catalog for more information!

Please contact the CMSE Graduate Director for additional information and updates.

Note: many of these classes are not directly affiliated with the CMSE program.

CMSE 890 (Sect. 301) - Programming Foundations for Bioinformatics - FS17

The CMSE Department will be offering a graduate course on Programming Foundations for Bioinformatics taught by faculty member Alexis Black Pyrkosz. This is an introductory course for scientists to learn programming in Unix and R with examples in bioinformatics.

CMSE 890 (Sect. 302) - Statistical Analysis and Visualization of Biological Data - FS17

The CMSE Department will be offering a graduate course on Statistical Analysis and Visualization of Biological Data taught by faculty member Alexis Black Pyrkosz. This is an introductory course for scientists to learn statistics that are relevant to bioinformatics.

CMSE 890 (Sect. 303) - Transcriptomic Data Analysis: from Reads to Functions - FS17

The CMSE Department will be offering a graduate course on Transcriptomic Data Analysis: from Reads to Functions taught by faculty member Alexis Black Pyrkosz.  This is an introductory course for scientists to learn how to turn raw RNA-Seq data into gene expression levels, differentially expressed genes, enriched pathways, and gene/sample clusters.

Spring 2017
CEM 993 - Advanced Topics of Quantum Theory, Chemistry - SS17

For student who are interested in learning how to better appreciate modern methods of many-body quantum mechanics, which are widely used in high accuracy first principles computations for atoms, molecules, condensed matter systems, and nuclei.

CHE 891 (Sect. 002) - Viscoelastic Fluids - SS17

The course is intended for advanced graduate students with an interest in developing a fundamental understanding of viscoelasticity. The class discussion will be supported by selected readings from the literature. Each student will have an opportunity to present oral presentations related to their emerging interest in memory fluids.

NEU 445 - Analysis of Neuroscience Data - SS17

Today’s technologies enable neuroscientists to gather data in quantities previously unimagined, and the BRAIN initiative will dramatically expand these capabilities. Many experimental researchers experience a bottleneck when it comes time to analyze their hard-won data. This course is intended to help neuroscience practitioners to meet the challenges posed by the analysis of large functional datasets, and to introduce quantitative students to the wealth of problems to be addressed in big data from neuroscience.

PHY 905 (Sect. 003) - Numerical Techniques in Astrophysics - SS17

Numerical solutions and statistical methods as applied to key problems in astronomy and astrophysics. N-body gravitational calculations, hydrodynamics in astronomy, radiative transfer.  Monte Carlo, Bayesian statistics, techniques for large datasets.

Fall 2016
BE 835 - Modeling Methods in Bio Engineering - FS16

Numerical methods, parameter estimation, and statistical analysis for mathematical models used in engineering. Theory will be illustrated with examples from various engineering disciplines. MATLAB will be taught and used in the class, and is required to be used for homeworks and exams.

CMSE 890 (Sect. 001) - Programming for Multi-Core Architectures - FS16

The CMSE department and the Blue Waters project at the University of Illinois is will be offering of a graduate course Algorithmic Techniques for Scalable Many-core Computing as a collaborative, online course for multiple participating institutions. The course includes online video lectures, quizzes, and homework assignments with access to free accounts on the Blue Waters system.

CSE 836 - Computational Comparative Genomics with Applications in Biology and Biomedicine - FS16

Can an algorithm save lives, fight disease, or shed light on human origins? This course covers computational aspects of comparative genomic analysis, an important tool used to investigate these and other cutting-edge questions in biology and human health. A primary goal of the course is to introduce quantitative foundations that underly this area and, more generally, bioinformatics and computational biology.

FOR/STT 875 - R Programming for Data Sciences - FS16

Programming in R and use of associated Open Source tools. Addressing practical issues in documenting workflow, data management, and scientific computing.

NEU 425 - Theory and computational models in neuroscience - FS16

The aims of this course are to introduce modeling techniques and issues, review successful models, and develop critical thinking about computational models and theories in neuroscience. We will study and experiment with simple models of single cells and small networks. We will then study some models of simple animal behaviors and conclude with more complex models for mammalian cognition. Students will learn and work in MATLAB and will make their own model of a specific dynamic process or behavior for a course project.

PLB 400/810 - Theories and practices in Bioinformatics - FS16

Introduction of the theories and algorithms behind widely used bioinformatics tools. Basic tool development by writing scripts in the Python programming language for data analysis.

Spring 2016
Fall 2015
NEU 445 - Analysis of Neuroscience Data - FS15

Today’s technologies enable neuroscientists to gather data in quantities previously unimagined, and the BRAIN initiative will dramatically expand these capabilities. Many experimental researchers experience a bottleneck when it comes time to analyze their hard-won data. This course is intended to help neuroscience practitioners to meet the challenges posed by the analysis of large functional datasets, and to introduce quantitative students to the wealth of problems to be addressed in big data from neuroscience.