Mastering Blackboard Learn: A Comprehensive Guide to RCC Tutorials
Blackboard Learn is a widely used virtual learning environment that provides a robust platform for online education. Many institutions are transitioning to newer, more streamlined versions like Blackboard Ultra, which offers an enhanced user experience. To ensure a smooth transition and effective utilization of the platform, educational institutions like Rogue Community College (RCC) and Boston University's Research Computing Services (RCS) group offer comprehensive tutorials. This article provides an in-depth look at Blackboard Learn, focusing on the tutorials and resources available, particularly those offered by RCC and RCS.
Transitioning to Blackboard Ultra
Rogue Community College is transitioning to Blackboard Ultra. To help faculty, RCC’s Blackboard administrators are hosting drop-in sessions throughout the summer. The Teaching and Learning Center (TLC) offers resources to help faculty navigate Ultra, including tutorials, videos, and training. A highlight is a collection of webinars by Blackboard’s Carolyn Ponce. Ultra Guides provide access to resources to ensure preparedness for the upcoming term.
Rockland Community College (RCC) Resources
Rockland Community College offers resources such as:
- SUNY Reconnect: Adult learners between 25 and 55 can earn an associate's degree for free in high-demand fields.
- Flex Start: Flexible and affordable class options are available. Enrollment is open for Flex Start 2 classes that begin on October 27.
- Campus Tours: Virtual and in-person tours are available to experience RCC.
RCS Tutorial Series Overview
The IS&T Research Computing Services (RCS) group offers a tutorial series on programming, data analysis, high-performance computing, and domain-specific topics three times each year. RCS tutorials teach practical concepts, techniques, and tools for research computing. Many sessions help users maximize their use of the Boston University Shared Computing Cluster (SCC). RCS staff can provide customized tutorial sessions tailored to specific needs.
Registration for the RCS Fall 2025 Training Series is open, using the new Terrier eDevelopment registration system. This system sends calendar invites, supports a waiting list, and has new features. Tutorial sessions are held in-person or over Zoom, with Zoom sessions being recorded.
Read also: Learn about Blackboard Learn at UD
Introduction to the Shared Computing Cluster (SCC)
The Linux cluster has more than 28000 processors and over 14 petabytes of storage available for Research Computing. A large number of software packages for programming, mathematics, data analysis, plotting, statistics, visualization, and domain-specific disciplines are available on the SCC.
The tutorial provides a general overview of the SCC and a hands-on introduction for new users, covering connecting to and using the SCC. It also covers basic Linux commands, with a recommendation to take the "Introduction to Linux" tutorial.
Introduction to Linux
The introductory tutorial covers a short history of Linux, logging in with ssh, the Bash shell and shell scripts, I/O redirection (pipes), file system navigation, and job control. Attendees can edit, compile, and run a simple C program. Instructions are provided for connecting to the SCC from a laptop before attending the tutorial.
Introduction to Python
The first part of the tutorial includes an introduction to basic types, if-statements, functions, lists, dictionaries, loops, and modules. The tutorial includes the use of a popular Python development environment and covers setting up Python on your own computer in addition to using Python on the SCC. This is a two-part tutorial.
Natural Language Processing (NLP) with Python and PyTorch
The tutorial explores the basics of NLP using Python and PyTorch, with no prior machine learning experience necessary. It covers the bigram character model and builds statistical and neural network implementations.
Read also: Learn Ultra Navigation
Machine Learning with Python and Scikit-Learn
This session introduces Scikit-Learn, a Python library for machine learning, which supports supervised and unsupervised learning and offers tools for data preprocessing, model fitting, model selection, and evaluation. Through hands-on exercises with real datasets, participants learn to develop models using algorithms such as linear regression, decision trees, random forests, K-means clustering, and dimensionality reduction. The session provides an overview of the general machine learning workflow and guidance on further ML resources.
To prepare, participants should ensure Python is installed on their machine. A conda environment file with necessary packages will be shared before the session, along with activation instructions. Prerequisites include experience with Python programming using Jupyter Notebook and familiarity with libraries like NumPy, Pandas, and Matplotlib.
Deep Learning with PyTorch
This session introduces PyTorch, a Python library for deep learning, optimized for GPU acceleration. Participants gain hands-on experience building and training neural networks for binary classification. Key topics covered include GPU acceleration using PyTorch Tensors, PyTorch Autograd for automatic differentiation, working with Data Datasets and Data Loaders in PyTorch, and building neural networks.
Preparation requires experience with Python programming, especially using Jupyter Notebook. Before the tutorial, Python must be installed, and a conda environment file with the required packages will be shared. Prerequisites include familiarity with the Python NumPy library and a basic understanding of machine learning and deep learning concepts. Attending the Machine Learning with Python Scikit-Learn tutorial is recommended for those new to machine learning.
Introduction to Hugging Face
This tutorial provides hands-on experience navigating the Hugging Face platform, exploring models that meet specific criteria, and building tools using these models. Participants will understand how Hugging Face supports modern AI development and how to leverage its tools effectively in their own projects. Key topics covered include an introduction to Hugging Face and its role in open-source AI, navigating the Hugging Face ecosystem and model hub, finding and evaluating models based on specific requirements, and understanding the platform’s tools, libraries, and community features.
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Preparation requires prior experience with Python programming (especially Jupyter Notebooks). Python must be installed, and a conda environment file will be provided in advance. Prerequisites include familiarity with the NumPy library and a basic understanding of machine learning and deep learning concepts. Attending the Machine Learning with Python (Scikit-Learn) and Deep Learning with PyTorch tutorials is recommended for those new to machine learning.
Introduction to MATLAB
MATLAB was originally developed for linear algebra and engineering problems but now has wide applicability and toolboxes for areas ranging from medicine, economics, and machine learning. The tutorial presents an introduction via solving hands-on example problems, motivating the syntax/tools in a “why” versus “what” way. Part One introduces participants to the user-interface and basic features including operators, loops, and conditionals. Part Two introduces participants to basic features including file reading/writing, functions, and text processing. No prior programming experience in any language is required.
Introduction to NumPy and SciPy
This tutorial is an introduction to the Numpy library, which provides data structures and algorithms that are optimized for numeric data. The Numpy library is the basis for a wide variety of numeric and graphics libraries in Python. The usage of the numpy multi-dimensional array type will be covered in detail. The Scipy library and how it can be effectively used with Numpy arrays and other Python data structures will be discussed. Familiarity with Python is assumed. If new to Python, registering for the "Introduction to Python" two-part tutorial is recommended.
Introduction to GIS Theory
The goal of this tutorial is to familiarize participants with common GIS terminology and concepts. In this session, the user interface of QGIS will be introduced, and simple workflows to get started on using the software, such as importing data, symbolizing data, and adding labels, will be covered. "Introduction to BU’s Shared Computing Cluster" is recommended for those planning to use the SCC for their GIS needs.
Introduction to ArcGIS Online
ArcGIS Online is a cloud-based web application that allows users to upload and store their own GIS data, save web maps, StoryMaps, and other ArcGIS Online applications in a centralized location. Users can control access permissions for their content and share completed work with the world or restrict access to only their working group. The tutorial covers the user interface and basic tools to manage content, manage permissions, and search for data. No software installation is required, just an internet connection and an internet browser. An ArcGIS Online account is needed.
Introduction to OpenStreetMap
This tutorial provides an overview of OpenStreetMap and shows how it is used by companies and organizations for some applications. It explores the data OpenStreetMap contains, how the data is structured, how to contribute information to OpenStreetMap, and what tools are available to access the data.
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