The Research Computing Center (RCC) provides the FSU research community with computational resources to enable scientific research
Our goal is to remove barriers between scientists and students and the resources needed to complete their work.
To facilitate computational research at FSU, we offer several training, outreach and support opportunities for using RCC and partner resources.
Please review the learning resources and online training below. If you have further questions, contact the RCC at email@example.com.
From live workshops to self-paced online courses, there are a variety of resources available to suite your learning style and research computing needs.
Workshops and Training Events | Workshops and training to introduce new users to RCC resources or focus on a specific software or science
In-Person Support | Request an appointment for face-to-face support and in-depth assistance on technical troubleshooting, grant writing or to chat about RCC systems
The RCC has curated the following LinkedIn Learning courses that are particularly relevant to the FSU research community. FSU students, faculty and staff have free access to all LinkedIn Learning online courses taught by industry experts.
- Learning Python | Python is one of the most-used dynamic languages and is supported on all major operating systems. This course gives you a comprehensive, accessible overview of Python 3 and covers topics required for day-to-day programming, including data types, data structures, operators and statements, control flow, loops, functions, classes, exception handling and file management. If you are a beginner programmer and want to learn how to write Python programs to accomplish a wide variety of tasks, join this course to start building a solid foundation for a Python career.
- Linux: Intro to the Command Shell | Knowledge of the Linux command line is critical for anyone who uses this open-source operating system. For many tasks, it is more efficient and flexible than a graphical environment. This course discusses the basics of working with the Linux command line using the Bash shell, focusing on practical Linux commands with examples that help you navigate through the file and folder structure, edit text and set permissions. NOTE: If you are using this tutorial to learn about the HPC, skip over "Section 1: Setting up your Environment" since you will be using the HPC as your environment.
- Learning SSH | Secure Shell (SSH) offers a safe way to communicate with a server and connect to systems remotely. This short course explains what SSH is and shows how to connect to an SSH server from different operating systems. It also demonstrates how to transfer files via SSH File Transfer Protocol (SFTP) and secure copy (SCP) and how to set up your own SSH server on Linux and Mac OS X.
- Learn Object-Oriented Design Principles | All good software starts with a great design. Object-oriented design helps developers plan applications before they write a single line of code and breaks down ideas into reusable and maintainable components. The course introduces you to the foundational concepts and terms—objects, classes, abstraction, inheritance and more—that you need to get started and shows you how to take the requirements for an app, identify use cases and map outclasses using Universal Modeling Language (UML). The final design can then be translated into code using an object-oriented programming language, such as Java, C#, Ruby or Python.
- Parallel and Concurrent Programming with C++ | Parallel programming unlocks a program’s ability to execute multiple instructions simultaneously. It is key to writing faster and more efficient applications. This training course introduces the basics of concurrent and parallel programming in C++, explains concepts like threading and mutual exclusion and demos them in action using C++.
- Python for Data Science Essential Training | Using Python to glean value from your raw data is critical. In this practical, hands-on course, learn how to use Python for data preparation, data munging, data visualization and predictive analytics. The course covers the essential Python methods for preparing, cleaning, reformatting and visualizing your data for use in analytics and data science. It helps to provide you with a working understanding of machine learning, as well as outlier analysis, cluster analysis and network analysis. Plus, the course explains how to create web-based data visualizations with Plot.ly and how to use Python to scrape the web and capture your own data sets.