Workshop Description

Introduction to Linux

Introduction to the Linux Command Line Interface for researchers

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.

Introduction to Programming in Python

This Workshop will introduce Python for those that have little to no programming experience and consists of three parts:

  • Python I: Introduction to Python and core Programming Concepts (No prior programming experience required).
  • Python II: Introduction to Numpy, Matplotlib and Anaconda Environments (Prerequisite: Python I)
  • Python III: Introduction to Data Analysis using Pandas (Prerequisite: Python I Recommended: Python II)

Introduction to Hadoop on Palmetto

This workshop introduces participants to the Hadoop ecosystem deployable on Palmetto. We will cover Hadoop’s architecture, how it can be deployed on Palmetto, import and export of big-data, basic usage, and how to submit scalable data analysis jobs. This workshop will incorporate the use of JupyterHub and Jupyter “Notebooks”. An understanding of the Linux command line and some Python experience is necessary.

Introduction to Big Data Analytics using Spark/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.

[Introduction to R Programming]

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.

Machine learning in 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 "R programming", offered by CITI team.

Machine learning in Python

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 Python programming language which utilizes abundance of scikit-learn package. 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 Python", offered by CITI team.

Deep learning in Python

This workshop provides an overall introduction to deep learning specifically with Python programming language which utilizes abundance of keras package. Such topics include: (1) Introduction to Deep Learning (2) Regression & Classification with Deep Learning (3) Image classification with CNN (4) Time series forecasting with RNN, LSTM

Pre-requisite for the course is "Machine Learning in Python", offered by CITI team.

Introduction to Applied Deep Learning: Object Detection Model using Tensorflow API

This workshop introduces how to apply the Python Tensorflow API to object detection, a common computer vision and deep learning concept. We will go through the steps involved in the process of creating a custom object detection model from scratch. This includes getting images, label them, train, evaluate, and export models on these images, and finally using the model to classify images.

Matlab

This is an introductory course 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.