Fall 2020 (Part 1) Registration is open!
You must have a Harvard University ID (HUID) to be able to register for a class. This is required to access the Harvard Training Portal.
|Class||Date||Time||Location||Seats||Training Materials||Registration (Single click directly registers the class)|
|Intro to O2||Wednesday, September 16, 2020||3-5p||Virtual||30||User Training github||link|
|Intro to Python||Wednesday, September 23, 2020||3-5p||Virtual||30||User Training github||link|
|No class||Wednesday, September 30, 2020|
|Intro to R/Bioconductor||Wednesday, October 7, 2020||3-5p||Virtual||30||User Training github||link|
|Intro to Matlab||Wednesday, October 14, 2020||3-5p||Virtual||50||User Training github||link|
|Intermediate O2||Wednesday, October 21, 2020||3-5p||Virtual||30||User Training github||link|
|Intro to O2||Wednesday, October 28, 2020||3-5p||Virtual||30||User Training github||link|
|Intro to Git/Github||Wednesday, November 4, 2020||3-5p||Virtual||30||User Training github||link|
|Intro to Parallel Computing||Thursday, November 12, 2020||3-5p||Virtual||50||User Training github||link|
|Intro to Python||Wednesday, November 18, 2020||3-5p||Virtual||30||User Training github||link|
|Intro to O2||Wednesday, November 25, 2020||3-5p||Virtual||30||User Training github||link|
|Intro to Python||Wednesday, December 2, 2020||3-5p||Virtual||30||User Training github||link|
|Intro to R/Bioconductor||Wednesday, December 9, 2020||3-5p||Virtual||30||User Training github||link|
Intro to O2
O2 for New Users addresses the needs of users who have very little linux experience, and are just getting started with HPC. More time will be devoted to covering linux basics, and the concepts of schedulers and jobs, and data management best practices. The lecture portion of this class is one hour, the second hour will be spent clinic-style with HMS RC staff to address workflow-specific questions and help convert commands to O2 SLURM syntax.
Intro to Python
Python is a popular scripting language for scientific computing and available across all computer platforms. The course will introduce you to some of the basics of the Python language as well as some of the nuances involved with its use specific to the O2 environment. The goal is to provide users with a foundational level of familiarity. Topics covered include basic data types and declaration, flow control (if/else), loops, a brief introduction to constructing a script, and a briefer introduction to modules. The course will be taught on O2, but general concepts are easily translatable to desktop and local installations.
Intro to R/Bioconductor
Intro to using R and Bioconductor. R is a powerful, open-source, highly adaptable statistical language useful for crunching numbers to datasets like those produced by next-gen sequencing. This class covers R basics and learning to think like/understand R. Users will learn how to set up personal R libraries on O2, and use O2 R for its high memory allocations and parallelization. Topics include how to install packages, learn about variables, data types. data manipulation, flow control, and functions, perform simple statistical tests, and create a variety of plots. Laptops are encouraged.
Intro to MATLAB
Matlab has become the “language of science” in the past few decades. It is simple to use, yet powerful enough to be productive on large computing infrastructures. If you need: 1) Fast prototyping of research ideas; or 2) avoid spending too much time in coding instead of doing real science by taking advantage of Matlab’s built-in functions; 3) User friendly graphical interface and educational documentation; 4) Simplicity of code; 5) Easy access to GPU computing power; 6) Easy plotting and presentation of data; you will find this introduction course useful. This course will introduce the basics of the MATLAB coding language with O2-scalability and data presentation.
Intro to Parallel Computing
This is a short introduction to Parallel Computing that will include an overview of the basic concepts of parallel programming: from running your job in an embarrassingly parallel way to writing simple shared and distributed memory parallelization codes in different languages. The seminar will cover several examples of actual parallel codes however it will not have any "hands on" components. A basic programming experience (of any language, no parallelization) is preferred in order to better follow the topics presented during the seminar.
Intermediate O2 is for current O2 users who would like to brush up on their bash skills, learn more advanced file transfer techniques, and unleash some of the powerful features of the SLURM scheduler.
Intro to Git and GitHub
This course introduces Git and GitHub and covers topics including: Getting Started with Git for version control, Using GitHub Desktop effectively, Collaborating with others on GitHub, and Utilizing GitHub Flow for better workflow. No previous exposure is assumed. We hope attendees will leave the class with the knowledge and tools necessary to start integrating Git into their workflows and excited to begin collaborating on GitHub.
Summer 2020 Part2 Registrations are open!
We invite you to attend the following free MATLAB webinars to develop your technical problem-solving skills and make better science.
MATLAB® helps you obtain deeper scientific and clinical insights by giving you the ability to analyze ever increasing amounts of data. As a platform, MATLAB is flexible and customizable, enabling you to include your own unique workflows and analytics, and allowing you to integrate with other tools. It scales to deal with large data or to combine different modalities and types of data. As a result, MATLAB has become a platform of choice for researchers.
|July 14th||Data Analysis and Visualization with MATLAB||Register|
|July 21st||Optimizing and Accelerating Your MATLAB Code||Register|
|July 28th||Parallel Computing with MATLAB||Register|
|August 4th||MATLAB for Medical Imaging Applications||Register|
|August 11th||Using MATLAB with Python||Register|
|August 18th||Text Analytics and Machine Learning with MATLAB||Register|
|August 25th||Data Science using MATLAB||Register|
|September 1st||Big Data: Scale Up to the Cloud with MATLAB||Register|
|September 8th||Machine Learning with MATLAB||Register|
|September 15th||Automated Image Labeling and Iterative Learning||Register|
|September 22nd||Deep Learning for Neuroscience||Register|
Additional computational trainings are available through other groups:
- Training from the Harvard Chan Bioinformatics Core
- Training from the Data Management Working Group
- Training from the SBGrid Consortium
- Training from BioGrids