We are thrilled to announce the development and release of 25 courselets as part of the FOUNT (Scaffolded, Hands-On Learning for a Data-Centric Future) project. These courselets are designed to provide hands-on, interactive learning experiences across a wide range of topics in data systems and computer science.
What Courselets Are Available?
Our courselets cover the following areas:
- Data Acquisition and Processing:
- Data Acquisition – Locally Generated Data: Learn how to collect data from local sensors using Raspberry Pi.
- Data Acquisition – Generic Remote Data: Acquire data from the web using Python for web scraping.
- Data Acquisition – Remote Experimental Data: Connect to remote data sources like the NSF-funded SAGE project.
- Machine Learning and Deployment:
- Connect Google Colab to a server on Chameleon: Learn how to integrate Google Colab with Chameleon.
- Using Cloud Servers for GPU-Based Inference: Perform fast inference on ML models using GPU-accelerated cloud servers.
- Using Edge Devices for CPU-Based Inference: Deploy ML models on edge devices for local inference.
- Reproducing “Deep Neural Nets: 33 Years Ago and 33 Years from Now”: Reproduce classic ML experiments and apply modern techniques.
- Deploy a Kubernetes Cluster: Learn how to set up and manage Kubernetes clusters for scalable applications.
- Deploying ML on Kubernetes: Sequence of activities to deploy ML models using Kubernetes, including load balancing and dynamic scaling. (GitHub Only)
- Machine Learning Process: An introductory courselet on the machine learning workflow.
- Storage Systems and Performance:
- Software RAID 0 (Disk Striping): Explore RAID configurations to improve disk performance.
- Benchmarking Disk I/O Performance: Measure and analyze disk input/output performance using tools like dd and FIO.
- Disk Partitioning Demo: Learn how to partition disks and manage file systems in Linux.
- Initializing a VM at KVM@TACC with Multiple Storage Volumes: Set up virtual machines with multiple storage volumes.
- Data Cleaning, Processing, & Analysis:
- Data Cleaning with Pandas: This courselet provides information on using Pandas to clean data.
- Data Analysis in Pandas: This courselet provides background information on using the Pandas library in Python.
- Data Visualization Using Charts: Create various charts to visualize and understand data effectively.
- Data Integration: Understand how to combine data from different sources into a unified view.
- Data Transformation: Learn techniques to convert data into formats suitable for analysis.
- Selection-Brushing: Learn techniques for selecting and highlighting data in visualizations.
- Introducing Python to the Geoscience Classroom: A beginner’s course on Python basics.
- Networks and Edge Computing:
- Network Emulation: Mimic specific network scenarios using network emulation techniques.
- Inspecting Network Traffic with tcpdump and Wireshark: Practice using tcpdump and Wireshark, two software applications for packet capture and packet analysis.
- Teaching Computer Networks on Chameleon: A collection of network topologies and configurations for teaching computer networks.
- AutoLearn Autonomous Cars: Educational module on autonomous vehicles leveraging edge computing and AI.
How to Access the Courselets
Almost all of our courselets are available on the Chameleon Trovi Portal. To find the FOUNT courselets:
- Create a Chameleon User Account (if you don’t have one already).
- Apply or renew your active project on Chameleon.
- Visit the Trovi Portal.
- Use the search filters:
- Select the “Education” tag.
- Look for artifacts with the “FOUNT” education badge.
Each courselet includes a description, launch instructions, and downloadable materials. They are designed to be self-contained and accessible to learners with varying levels of expertise.
Get Started Today
Whether you’re an educator looking to enhance your curriculum or a student eager to expand your skills, our courselets provide practical, hands-on learning experiences. We invite you to explore these resources and embark on your data-centric learning journey.
