Registration open for Research Computing Spring 2020 seminar series

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HPC Research Computing Spring 2020 Seminar Series

Research Computing is pleased to announce a weekly seminar series beginning January 31, 2020. All sessions will be held at 1:10 p.m. - 3:00 p.m. in the EWFM Computing Center Rm 292. Visit go.lehigh.edu/ltsseminars to register.

Please forward to your colleagues who will benefit from these seminars. Seminar slides will be posted at https://researchcomputing.lehigh.edu/training.

The seminar schedule is as follows:

January 31: Research Computing Resources at Lehigh

Description: This training provides an overview of Research Computing resources available to the Lehigh research community.

Instructor: Alex Pacheco

February 7: Linux: Basic Commands & Environment

Description: Linux is a free and open source operating system that is the OS of choice of the world's leading supercomputers as well as LTS HPC clusters and some computer labs at Lehigh. This session will provide an introduction to the linux/unix environment, command line basics, logging in to remote system, transferring data, vi/emacs editors etc to get started with using a Linux/Unix based computer. This training is geared towards researchers who want to learn or need to learn how to use a linux/unix based resource.

Instructor: Alex Pacheco

February 14: Using SLURM scheduler on Sol

Description: This training provides a hands on introduction to using the SLURM scheduler to submit and monitor jobs. SLURM is the scheduler on Lehigh's HPC resource, Sol, and national supercomputing resources including XSEDE and NERSC.

Prerequisites:
    An HPC Level 2 (https://idmweb.cc.lehigh.edu/accounts/auth/?page=hpc) account or an account on national supercomputing resources (XSEDE, DOE, etc) that uses SLURM.
    Familiarity with Linux/Unix environment, basic command and *nix editors such as vi or emacs is mandatory.

Instructor: Alex Pacheco

February 21: Python Programming

Description: Python is an interpreted high-level programming language for general-purpose programming popular in scientific computing and data analysis. In this seminar, you will learn the basics of Python, including language fundamentals and basic programming.

Prerequisites:
  Programming background is beneficial but not required.
Instructor: Sachin Joshi

February 28: Programming in R

Description: In this tutorial, you will learn the basics of R, including language fundamentals, data types, functions and basic programming including File I/O.

Prerequisites:
    Programming background is beneficial but not required.
Instructor: Jeremy Mack

March 6:  Data Visualization with Python

Description: This seminar provides a hands on introduction to Data Visualization using the Python programming language.

Prerequisites:
  Programming background in Python is required.

Instructor: Sachin Joshi

March 20: Data Visualization with R

Description: This tutorial provides a hands on introduction to Data Visualization using the R programming language

Prerequisites:
    Programming background in R is required.
Instructor: Jeremy Mack

March 27: Object-Oriented Programming with Python

Object Oriented Programming or OOP is a programming paradigm which provides a means of structuring programs (combining data and functionality) so that properties and behaviors are bundled into individual objects. In this seminar, you’ll learn the basic concepts of OOP in Python: Python Classes, Object Instances, Defining and Working with Methods and OOP Inheritance.

Prerequisites: Programming background in Python is required. Knowledge of OOP’s concepts is beneficial but not required.
Instructor: Sachin Joshi

April 3 & 10: Machine Learning

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence (AI) based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. In this two part seminar you’ll learn the basic machine learning concepts: supervised and unsupervised learning, classification and regression algorithms .

Prerequisites: Basic knowledge of general CS concepts such as data structures, linear algebra and design of algorithm is required.
Instructor: Sachin Joshi