Description: This short course introduces basic computational environments and tools to graduate students with limited prior experience. It will provide an introduction to UNIX systems, software compilation / installation, cluster job management as well as data formats, management, and visualization. A brief introduction to scripting programming languages will also be presented.
Course Goals: Students enrolled in the class will develop skills to accelerate their research in computational research environments. Topics will include an intensive introduction to (a) UNIX systems and software management, (b) data processing and simple programming, (c) data formats and visualization, and (d) software version and cluster control. This training will provide a computational foundation that will allow students to focus on the theoretical and biological aspects of their research.
Competencies: After completing this class, students are expected to be able to attain the following competencies:
-Navigate and organize UNIX files and folders
-Compile and install software in UNIX environments
-Understand basic programming data structures and processes
-Create simple scripts to manage and analyze data
-Utilize and apply popular file formats to modern large-scale data sets
-Apply proper visualization tools and strategies to view data
-Utilize software versioning technologies for documenting and organizing software
-Utilize high-throughput computing clusters for parallel data processing
This course is cross-listed with Biostat 606 = HG 606 = Bioinfo 606.
Prerequisites: BIOSTAT601, BIOSTAT602 and BIOSTAT615 or equiv and proficiency in C++
Description: Modern numerical analysis for statisticians. Combination of theory and practical computational examples illustrating the current trends in numerical analysis relevant to probability and statistics. Topics choose from numerical linear algebra, optimization theory, quadrature methods, splines, and Markov chains. Emphasis on newer techniques such as quasi-random methods of integration, the EM algorithm and its variants, and hidden Markov chains. Applications as time permits to areas such as genetic and medical imaging.