To meet the growing need for computational and bioinformatics skills for trainees, the CCBB sponsors two peer-led working groups geared towards (1st year) graduate students and postdocs, who are just starting new research projects that will involve programming and statistics, but who have little or no background in the area. The working groups provide informal weekly sessions that facilitate the transfer and sharing of knowledge. Each session has a period devoted to solving problems contributed by the participants and provide a cheat sheet (posted on the CCBB wiki) that can serve as a future reference.
Programming is becoming an ever-more fundamental part of biological research. To help foster the growing Biocomputing community here at UT, The Center for Computational Biology and Bioinformatics will again sponsor a Peer-led Working Group on Computing in Biology during the spring semester.
What it is: A no-pressure learning environment where graduate students and post-docs can learn the basics of programming. This is not a for-credit course. Its purpose is not to replace any existing courses, but to bolster the programming community here and provide support for those who want to learn but don’t know where to begin.
Who it’s for: The course is geared towards graduate students and post-docs, who are just starting new research projects that will involve programming, but who have little or no background in the area. More experienced programmers are welcome to attend! We would especially like to welcome experienced researchers to attend our open-coding hour (see below) and be part of the growing bio-computing community.
Whatever your background or experience, we encourage you to attend.
If you have any questions, please contact:
Practicum course for beginners on tools used in modern biology. In hands-on exercises students will build their own Linux virtual machine, learn how to process data on the command line, and learn the fundamentals of programming in Python. This class is for students without prior knowledge of command-line software tools or programming. Students must bring their own laptops, which must be powerful enough to run a virtual machine; the class requires 20 GB of hard drive space on the student laptops.
Instructor: James Derry (jderry[at]austin.utexas.edu)
In the fall semester of each year, Integrative Biology graduate students host a weekly peer-led course about data analysis using the programming language, R. The goal of this workshop is to provide graduate students early in their studies with a broad set of programmatic tools that can be used to carry out their own scientific data analysis projects. The course is tailored for students with no prior programmatic experience, and will loosely follow the R for Data Science textbook (http://r4ds.had.co.nz/).
Please visit the course website (https://ccbbatut.github.io/rstats_fall2017/) to learn more about the course and participate!
1. Coursera sepecialization on bioinformatics : This describes algorithms and approaches used in bioinformatics. It has a course on genome sequencing. https://www.coursera.org/specializations/bioinformatics#courses
2. Coursera specialization on data science: This is the John Hopkins data science series- It covers R, machine learning, visualization etc. https://www.coursera.org/specializations/jhu-data-science
3. Coursera specialization on genomic data science: https://www.coursera.org/specializations/genomic-data-science
4. Data camp has two tracks for data science- one using R and one using python.