In addition to providing free, in-person workshop training, School of Medicine Research Computing staff teach for-credit courses. Below is a selection of courses that SOMRC has taught, co-taught or provided guest lectures.
BIMS 8382: Introduction to Biomedical Data Science Spring 2017
This course introduces methods, tools, and software for reproducibly managing, manipulating, analyzing, and visualizing large-scale biomedical data. Specifically, the course introduces the R statistical computing environment and packages for manipulating and visualizing high-dimensional data, covers strategies for reproducible research, and culminates with analysis of data from a real RNA-seq experiment using R and Bioconductor packages.
School of Medicine Research Computing provides training opportunities covering a variety of data analysis, basic programming and computational topics. All of the classes listed below are taught by SOMRC experts and are freely available to UVa faculty, staff and students.
For a comprehensive list of all such educational resources visit the Computation and Data Resource Exchange (CADRE) education portal.
Upcoming – Fall 2017 Registration is now open! Mark these dates on your calendar.
Domino Data Lab (DDL) provides a central environment and features for data science projects including project management, collaboration with team members, and setting up hardware configuration for a project.
Account Request Access to DDL to Ivy is managed through the Ivy account request process. Accounts are issued on a per project basis, with PIs (and any project members) being granted individual accounts to log into the DDL platform.
Once the request has been approved and all associated members have completed the necessary documentation, each individual project member can sign into DDL with his / her UVa Eservices user name and password.
This workshop will cover fundamental concepts for creating effective data visualization and will introduce tools and techniques for visualizing large, high-dimensional data using R. We will review fundamental concepts for visually displaying quantitative information, such as using series of small multiples, avoiding “chart-junk,” and maximizing the data-ink ratio. After briefly covering data visualization using base R graphics, we will introduce the ggplot2 package for advanced high-dimensional visualization. We will cover the grammar of graphics (geoms, aesthetics, stats, and faceting), and using ggplot2 to create plots layer-by-layer.
Data analysis involves a large amount of janitor work - munging and cleaning data to facilitate downstream data analysis. This workshop is designed for those with a basic familiarity with R who want to learn tools and techniques for advanced data manipulation. It will cover data cleaning and “tidy data,” and will introduce participants to R packages that enable data manipulation, analysis, and visualization using split-apply-combine strategies. Upon completing this lesson, participants will be able to use the dplyr package in R to effectively manipulate and conditionally compute summary statistics over subsets of a “big” dataset containing many observations.
School of Medicine Research Computing can help with accessing, preparing, visualizing and analyzing data. We can assist you by implementing your analysis strategy in an appropriate computing language, including R, Python and Matlab. We will work with you to prepare scripts that are are reproducible, efficient and flexible.
Manipulation Data analysis generally involves a significant effort to transform, aggregate, subset or otherwise prepare a dataset. That could include dealing with missing values as well as merging or joining multiple datasets.