Harvard University is offering free online course on High-performance Computing for Reproducible Genomics. Students will discuss how to create interactive reports that enable us to move beyond static tables and one-off graphics so that our analysis outputs can be transformed and explored in real time.
In this four week course, applicants will learn how to bridge from diverse genomic assay and annotation structures to data analysis and research presentations via innovative approaches to computing. This course will start on September 7, 2017.
Course At A Glance
Length: 4 weeks
Effort: 2-4 hours pw
Subject: Biology & Life Sciences
Institution: Harvard University and edx
Certificate Available: Yes, Add a Verified Certificate for $49
Session: Course Starts on September 7, 2017
Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a difference globally. Harvard faculty are engaged with teaching and research to push the boundaries of human knowledge. The University has twelve degree-granting Schools in addition to the Radcliffe Institute for Advanced Study.
About This Course
If you’re interested in data analysis and interpretation, then this is the data science course for you.
Enhanced throughput: Almost all recently manufactured laptops and desktops include multiple core CPUs. With R, it is very easy to obtain faster turnaround times for analyses by distributing tasks among the cores for concurrent execution. University will discuss how to use Bioconductor to simplify parallel computing for efficient, fault-tolerant, and reproducible high-performance analyses. This will be illustrated with common multicore architectures and Amazon’s EC2 infrastructure.
Enhanced interactivity: New approaches to programming with R and Bioconductor allow researchers to use the web browser as a highly dynamic interface for data interrogation and visualization. University will discuss how to create interactive reports that enable us to move beyond static tables and one-off graphics so that our analysis outputs can be transformed and explored in real time.
Enhanced reproducibility: New methods of virtualization of software environments, exemplified by the Docker ecosystem, are useful for achieving reproducible distributed analyses. The Docker Hub includes a considerable number of container images useful for important Bioconductor-based workflows, and University will illustrate how to use and extend these for sharable and reproducible analysis.
- Parallel Computing
- Interactive Graphics
- Reproducible distributed analysis
Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute.
Vincent Carey is Associate Professor of Medicine (Biostatistics) in the Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School.
Michael Love is a postdoctoral fellow with Dr. Irizarry in the Department of Biostatistics at the Dana Farber Cancer Institute and Harvard T.H. Chan School of Public Health.
How To Join This Course
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