FSU students, staff, and faculty have access to LinkedIn Learning, an online resource with thousands of video courses taught by industry experts. RCC has curated some courses we think are particularly relevant to the FSU Research Community.
If you haven't checked out LinkedIn Learning yet, be sure to head over to the FSU Login Portal, where you can find the full library of training videos.
- Learning Python — Python—the popular and highly readable object-oriented language—is both powerful and relatively easy to learn. Whether you're new to programming or an experienced developer, this course can help you get started with Python. Joe Marini provides an overview of the installation process, basic Python syntax, and an example of how to construct and run a simple Python program. Learn to work with dates and times, read and write files, and retrieve and parse HTML, JSON, and XML data from the web.
- 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's more efficient and flexible than a graphical environment. In this course, experienced instructor Scott Simpson 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're using this tutorial to learn about the HPC, feel free to skip over "Section 1: Setting up your Environment" since you'll be using the HPC as your environment.
- Learning SSH — Secure Shell (SSH) offers a safe way to communicate with a server and to connect to systems remotely. Consequently, SSH is a vital skill for anyone who works in IT. In this short course, Scott Simpson explains what SSH is and shows how to connect to an SSH server from different operating systems. He 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 break down ideas into reusable and maintainable components. This course focuses on the foundational concepts, teaching them in a fun, interactive way to help you quickly develop your skills. Tag team Olivia and Barron Stone introduce you to the concepts and terms—objects, classes, abstraction, inheritance, and more—that you need to get started. They then show 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 one of the many popular object-oriented programming languages, 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 increases the overall processing throughput and is key to writing faster and more efficient applications. This training course introduces the basics of concurrent and parallel programming in C++, providing the foundational knowledge you need to write more efficient, performant code. Instructors Barron and Olivia Stone explain concepts like threading and mutual exclusion in a fun and informative way, relating them to everyday activities you perform in the kitchen. To cement the ideas, they demo them in action using C++.
- Python for Data Science Essential Training — By using Python to glean value from your raw data, you can simplify the often complex journey from data to value. In this practical, hands-on course, learn how to use Python for data preparation, data munging, data visualization, and predictive analytics. Instructor Lillian Pierson, P.E., covers the essential Python methods for preparing, cleaning, reformatting, and visualizing your data for use in analytics and data science. She helps to provide you with a working understanding of machine learning, as well as outlier analysis, cluster analysis, and network analysis. Plus, Lillian 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.