Cyberinfrastructure Technology Integration

Clemson Computing and Information Technology (CCIT) provides research cyberinfrastructure resources and advanced research computing capabilities through its Cyberinfrastructure Technology Integration (CITI) group.

Training Workshops

CITI partners with researchers across campus and across the country to offer a diverse catalog of advanced computing training opportunities for Clemson University students, researchers, faculty, and staff, as well as opportunities for our external partners at other universities and organizations. If you have problems with or questions about course registration, please email ithelp@clemson.edu with the words “Palmetto training” in the subject.

All workshops listed below are free for Clemson University students, faculty and staff. Registration is required, and can be done at https://cucourse.app.clemson.edu/it-training/student-index.php one week prior to the listed start dates.

Summer 2020 Schedule of Workshops

Introduction to Linux

Introduction to the Linux Command Line Interface for researchers

Location: Online via zoom

  • Friday, May 22, 9:00AM - 12:00PM. Building/Room: Online via Zoom
  • Monday, Jul 20, 9:00AM - 12:00PM. Building/Room: Online via Zoom

Introduction to Research Computing on Palmetto Cluster

This workshop introduces participants to the Palmetto Cluster–Clemson University’s largest high-performance computing resource–its structure and basic usage and how to submit computational tasks to the cluster.

Location: Online via Zoom

  • Monday, May 25, 9:00AM - 12:00PM Building/Room: Online via Zoom
  • Thursday, Jul 23, 9:00AM - 12:00PM. Building/Room: Online via Zoom

Introduction to Programming in Python

This 3 Part workshop introduces participants to programming, using the Python programming language, and is built around common scientific tasks such as loading, analyzing and visualizing data. The intended audience is researchers or students with no prior programming experience. Part 1 focuses on general introduction to python, type, control structure and numpy; Part 2 focuses more on matplotlib packages and scipy; Part 3 gives you more information on pandas

Location: Online via Zoom

  • Part 1 - Monday, May 18, 9:00AM - 12:00PM. Building/Room: Online via Zoom
  • Part 2 - Wednesday, May 20, 9:00AM - 12:00PM. Building/Room: Online via Zoom
  • Part 3 - Friday, May 22, 9:00AM - 12:00PM. Building/Room: Online via Zoom
  • Part 1 - Monday, Jun 22, 9:00AM - 12:00PM. Building/Room: Online via Zoom
  • Part 2 - Wednesday, Jun 24, 9:00AM - 12:00PM. Building/Room: Online via Zoom
  • Part 3 - Friday, Jun 26, 9:00AM - 12:00PM. Building/Room: Online via Zoom

Introduction to Data Science using R

Introduction to R language for data analytics using RStudio on PC and also Jupyter notebooks on Palmetto. Workshop contents include basic understand of R, installation of additional R modules, introduction to data manipulation, introduction to visualization, and several best practices for using R. No prior knowledge of R or programming in general is required. Part 1 focuse on introduction to Data Science, R prgramming language with control structure; Part 2 gives more information on function, parallel programming, profiling and plotting using ggplot2 packages

Location: Online via Zoom

  • Part 1 - Tuesday, May 26 9:00AM - 12:00PM. Building/Room: Online via Zoom
  • Part 2 - Tuesday, Jun 02, 9:00AM - 12:00PM. Building/Room: Online via Zoom

Introduction to Machine Learning for Research using R

Machine learning is the science of teaching computers to reproduce the assigned procedure without being explicitly programmed. It has been used in many practical applications such as self-driving cars, speech recognition, email spam classification. It has been widely used not only in engineering (hydroinformatics, bioinformatics, genomics, geosciences and remote sensing, mechatronics) but also in economy, health sciences and even in real estates industry. This workshop provides an overall introduction to machine learning specifically with R programming language which utilizes abundance of R statistical packages. Such topics include: (1) Supervised learning (regression analysis, distance-based algorithm, regularization algorithm, tree-based algorithm, Bayes algorithm, support vector machines, artificial neural networks). (2) Unsupervised learning (clustering, dimensionality reduction). The course will also draw from numerous case studies and applications that can be applied in different engineering programs.

Pre-requisite for the course is “Introduction to Data Science using R”, offered by CITI team.

Location: Main Campus (Clemson)

    We do not offer this online workshop in summer 2020

Advanced analytics in Business using Rapidminer

Advanced analytics goes beyond business intelligence by using sophisticated modeling techniques to predict future events or discover patterns which cannot be detected otherwise. This workshop is for students from Non-STEM backgrounds to learn about Data Science and Machine Learning platform using Rapidminer. No programming experience required

Location: Main Campus (Clemson)

    We do not offer this online workshop in summer 2020

Introduction to Matlab

This is an introductory and advance courses about data analysis with MATLAB. We will cover such topics as the MATLAB interface, flow control and loops, working with vectors and matrices, using scripts and functions, and plotting. No prior knowledge of MATLAB or programming in general is required for the first part. In the advanced workshop, we will be covering parallel computing and how to run Matlab in HPC Palmetto

Location: Main Campus (Clemson)

  • Matlab - Tuesday, Jun 23, 9:00AM - 12:00PM. Online via Zoom

Introduction to Big Data Analytics in Python

This workshop will teach how to how to utilize Apache Spark and Python to perform large-scale, in-memory data analytics. Learning outcomes of this workshop include understanding the overall conceptual design of Spark and demonstrate the advantages of using Spark over traditional Hadoop MapReduce. Participants will also learn to develop Spark programs using Python and to leverage Spark’s specific capabilities such as SQLContext and DataFrame to assist with data analytics. Most importantly, we will teach you how to leverage Clemson University’s Palmetto Cluster to run large-scale, in-memory data analytics.

Location: Online via Zoom

  • Part 1 - Friday, Jun 12, 9:00AM - 12:00PM. Building/Room: Online via Zoom
  • Part 2 - Friday, Jun 19, 9:00AM - 12:00PM. Building/Room: Online via Zoom

Introduction to D3.js Workshop

The Clemson Data Visualization Lab is pleased to announce the first Introduction to Data Visualization with D3.js workshop. D3.js is one of the most promising data analytics and visualization platforms based on JavaScript that allows for interactive and web-based exploration of data and stunning visualization. In this workshop you'll learn how to: · Import and display data in a flexible, dynamic, and interactive way · Let others explore your data via interactive elements · Create webpages, add elements, and bind data to those elements through charts, graphics, and maps This is asynchronous training, where you can go over the instructional materials at your own pace. Instructions are available at https://sites.google.com/g.clemson.edu/vizlab/introduction-to-data-visualization-with-d3-js

Location: Online via Zoom

  • Part 1 - Friday, Jul 3rd, 10:00AM - 11:00AM. Zoom: https://clemson.zoom.us/j/93997645157
  • Part 2 - Friday, Jul 3rd, 01:00PM - 02:00PM. Zoom: https://clemson.zoom.us/j/94046895771

GIS Training

For GIS Training, please visit Clemson Center for Geospatial Technologies.

Certificate

Certificate for all CITI-ACDS workshops are here: