IS&T RCS Fall 2025 Trainings

September 8 – October 2, 2025

Registration for the RCS Fall 2025 Training Series is now open! Recordings and slides from past tutorials by RCS staff and vendors are also available in the training section of the RCS website.

Note that we last Fall switched to using a new registration system, Terrier eDevelopment, that will send calendar invites to attendees, supports a waiting list, and has various other new features. For those who have taken our tutorials earlier, the new registration process is quite different. Follow the process outlined here to register for tutorials.

  1. Go to the tutorial you want to register for by clicking on its name or just scrolling down on this page.
  2. Click the appopriate green ‘Register for this session’ link.
  3. This will take you to a page on Terrier eDevelopment. Click the blue ‘Register’ button which will take you to another page on Terrier eDevelopment.
  4. Click the blue ‘Add’ button and then click the blue ‘Register’ button on the bottom right of that same page.
  5. If you wish to register for additional tutorials, return to this page and follow the same process for each.
  • For hands-on sessions where you wish to use your own computer, please have the appropriate software installed on your computer before the session starts.
  • Tutorials are tagged based on experience required (Beginner, Intermediate, or Advanced), location (details below), and if they are new.
  • Tutorial sessions are held either in-person or over Zoom. Note that Zoom sessions will be recorded; keep your camera off if you do not want your image recorded. The recorded sessions may be made available to the BU community.

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. These tutorials are free and open to all members of the Boston University community.

RCS tutorials teach practical concepts, techniques, and tools for research computing that you can use in any environment. Many sessions are specifically designed to help you maximize your use of the Boston University Shared Computing Cluster (SCC). Additionally, RCS staff can provide customized tutorial sessions tailored to your course, research group, or lab’s specific needs. Please contact us at help@scc.bu.edu if you are interested.

Trainings Schedule

You may register for as many tutorials as you like. Registration is required and is accessed with your BU Kerberos password.

If you don’t have a Kerberos password, or if you find that a tutorial is full, or have any other questions, please send email to rcs-tutorial@bu.edu.

Tutorial Locations

BSC Biological Science Center, 2 Cummington Mall, Room 107
Zoom Online over Zoom After you register, you will be sent a calendar invite that includes the Zoom link.

Tutorial Descriptions and Times

Research Computing Basics Tutorials

BeginnerIntroduction to BU's Shared Computing Cluster (Hands-on)

Instructor: Aaron Fuegi (aarondf@bu.edu)

BSCThursday September 11, 2025 1:00pm - 3:00pm
This tutorial will introduce Boston University's Shared Computing Cluster (SCC) in Holyoke, MA. This Linux cluster has more than 28000 processors and over 14 petabytes of storage available for Research Computing by students and faculty on the Charles River and BUMC campuses. A very large number of software packages for programming, mathematics, data analysis, plotting, statistics, visualization, and domain-specific disciplines are available as well on the SCC. You will get a general overview of the SCC and the facility that houses it and then a hands-on introduction covering connecting to and using the SCC for new users. This tutorial will cover a few basic Linux commands but we strongly encourage people to also take our more extensive "Introduction to Linux" tutorial. There will also be ample time for questions of all types about the SCC. For those in the BU community interested in using a particular package on the SCC, after taking this tutorial we also recommend viewing one of our short videos on that package if one is available.   Please read and follow these instructions prior to attending the tutorial.

BeginnerIntroduction to Linux (Hands-on)

Instructor: Augustine Abaris (augustin@bu.edu)

BSCMonday September 15, 2025 9:30am - 11:30am
This tutorial will give attendees a hands-on introduction to Linux. Topics covered will include 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. Time permitting, attendees will edit, compile, and run a simple C program. If you have not connected to the SCC from your laptop before, please read and follow these instructions prior to attending the tutorial.

BeginnerData Preparation for Neuroimagers: BIDS, mriqc, and fmriprep (Hands-on)

Instructor: Kyle Kurkela (kkurkela@bu.edu)

ZoomFriday September 19, 2025 1:00pm - 3:00pm
This tutorial will introduce Boston University’s Shared Computing Cluster (SCC) for use with Magnetic Resonance (MR) neuroimaging methods focusing on data preparation. We will work hands-on with the SCC to highlight the neuroimaging tools available on the SCC for importing, organizing, assessing the quality of, and preprocessing neuroimaging data. Software covered will include dcm2bids, MRIQC, and fMRIPrep. Topics covered will include the Brain Imaging Data Structure (BIDS), converting DICOM images to the BIDS standard using dcm2bids, running a quality assessment routine using MRIQC, and preprocessing your data using fMRIPrep. To get the most out of this workshop, attendees will want to have some basic familiarity with working in Linux environments.

IntermediateIntermediate Usage of the SCC (Lecture)

Instructor: Katia Bulekova (ktrn@bu.edu)

ZoomThursday October 2, 2025 10:00am - 12:00pm
This tutorial will provide some more advanced techniques and common strategies used for interacting with the Shared Computing Cluster and its resources. The topics discussed during the tutorial include:
  •    Customizing your environment
  •    Parallel computing on the SCC
  •    Jobs monitoring and profiling: CPU and GPU utilization, memory usage
  •    Profiling programs for performance optimization
  •    General optimization strategies
Prerequisites: some prior experience with high performance computing or attendance of our “Introduction to BU's Shared Computing Cluster” tutorial.

Computer Programming Tutorials

BeginnerIntroduction to Python, Part One (Hands-on)

Instructor: Brian Gregor (bgregor@bu.edu)

BSCTuesday September 9, 2025 10:00am - 12:00pm

This is an introduction to the essential features of Python. This 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 so please remember to sign up for both sessions.

If you do not have Python installed on your home machine, please read and follow these instructions prior to attending the tutorial.

BeginnerIntroduction to Python, Part Two (Hands-on)

Instructor: Brian Gregor (bgregor@bu.edu)

BSCThursday September 11, 2025 10:00am - 12:00pm

This tutorial is a continuation of "Introduction to Python, Part One" and introduces more features of the language, common libraries such as numpy and matplotlib, and the basics of debugging Python programs. Please make sure you sign up for part one as well.

If you do not have Python installed on your home machine, please read and follow these instructions prior to attending the tutorial.

BeginnerNatural Language Processing Basics (Hands-on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSCTuesday September 9, 2025 1:00pm - 3:00pm
Human language/communication can be studied computationally through Natural Language Processing (NLP). We'll explore the basics of NLP using Python and PyTorch; no prior machine learning experience necessary, basic Python knowledge is helpful but not necessary. We will look at the bigram character model and build statistical and neural network implementations. The core concepts we will examine are applicable beyond our introductory example to neural networks/NLP in general.

IntermediateMachine Learning with Python scikit-learn, Part One (Hands-on)

Instructor: Atish Kamble (akamble@bu.edu)

ZoomTuesday September 16, 2025 1:00pm - 3:00pm

This is the first part of a two-part tutorial series. Be sure to also register for Part Two on Thursday, Feb 6 from 1:00pm to 3:00pm to continue building your knowledge.

What to Expect: This session introduces Scikit-Learn, a powerful Python library for machine learning. Scikit-Learn supports supervised and unsupervised learning and offers tools for:

  • Data preprocessing
  • Model fitting
  • Model selection
  • Evaluation
  • And much more

Through hands-on exercises with real datasets, you'll learn to develop models using modern algorithms, including:

  • Linear regression
  • Decision trees and random forests
  • K-means clustering
  • Dimensionality reduction

We'll also provide an overview of the general machine learning workflow and wrap up with guidance on further ML resources.

Preparation: If Python is not installed on your machine, follow these instructions.

A conda environment file with all necessary packages will be shared before the session, along with activation instructions.

Prerequisites: Experience with Python programming using Jupyter Notebook and familiarity with libraries like NumPy, Pandas, and Matplotlib.

Get ready to explore the capabilities of Scikit-Learn and start building practical machine learning solutions!

IntermediateMachine Learning with Python scikit-learn, Part Two (Hands-on)

Instructor: Atish Kamble (akamble@bu.edu)

ZoomThursday September 18, 2025 1:00pm - 3:00pm

This is the second part of a two-part tutorial series. Be sure to also register for Part One on Tuesday, Feb 4 from 1:00pm to 3:00pm.

What to Expect: This session introduces Scikit-Learn, a powerful Python library for machine learning. Scikit-Learn supports supervised and unsupervised learning and offers tools for:

  • Data preprocessing
  • Model fitting
  • Model selection
  • Evaluation
  • And much more

Through hands-on exercises with real datasets, you'll learn to develop models using modern algorithms, including:

  • Linear regression
  • Decision trees and random forests
  • K-means clustering
  • Dimensionality reduction

We'll also provide an overview of the general machine learning workflow and wrap up with guidance on further ML resources.

Preparation: If Python is not installed on your machine, follow these instructions.

A conda environment file with all necessary packages will be shared before the session, along with activation instructions.

Prerequisites: Experience with Python programming using Jupyter Notebook and familiarity with libraries like NumPy, Pandas, and Matplotlib.

Get ready to explore the capabilities of Scikit-Learn and start building practical machine learning solutions!

BeginnerIntroduction to Julia, Part One (Hands-on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSCTuesday September 23, 2025 10:00am - 12:00pm
Julia is a high-performance programming language, but with many features more common to lower performance interpreted languages. Many of its features are well suited for numerical analysis and computational science, with functions and syntax that are built around supporting this. This tutorial presents an introduction via solving hands-on example problems; this motivates the syntax/tools in a "why" versus "what" way. The tutorial introduces participants to common ways of using Julia and basic features including operators, loops, conditionals, and functions. This is a two-part tutorial so please also remember to sign up for part two.

BeginnerIntroduction to Julia, Part Two (Hands-on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSCThursday September 25, 2025 10:00am - 12:00pm
This tutorial is a continuation of Introduction to Julia, Part One. We strongly recommend that if you are interested in this tutorial, you also register for Part One.

IntermediateDeep Learning with PyTorch, Part One (Hands-on)

Instructor: Atish Kamble (akamble@bu.edu)

ZoomTuesday September 23, 2025 1:00pm - 3:00pm

This is the first part of a two-part tutorial on PyTorch. Be sure to also register for Part Two on Thursday, February 13, from 1 PM to 3 PM to continue building your knowledge.

What to Expect: This session introduces PyTorch, a popular and versatile Python library for deep learning, optimized for acceleration processing using GPUs. You’ll gain hands-on experience building and training neural networks for binary classification.

Key Topics Covered:
  • Why PyTorch?
  • GPU acceleration using PyTorch Tensors
  • PyTorch Autograd for automatic differentiation
  • Working with Data
  • Datasets and Data Loaders in PyTorch
  • Building Neural Networks
  • Developing deep learning models for binary classification using PyTorch
Preparation:
  • Experience with Python programming, especially using Jupyter Notebook, is required.
  • Before the tutorial, ensure Python is installed on your machine. Detailed setup instructions and a conda environment file with the required packages will be shared.
  • If you plan to use your own computer, the conda environment must be installed and activated in advance.
Prerequisites:
  • Familiarity with Python NumPy library.
  • Basic understanding of machine learning and deep learning concepts and experience in using them.

Additional Recommendation: For those new to machine learning, consider attending the preceding tutorials on Machine Learning with Python Scikit-Learn to build foundational knowledge.

Get ready to dive into PyTorch and create powerful deep learning models!

IntermediateDeep Learning with PyTorch, Part Two (Hands-on)

Instructor: Atish Kamble (akamble@bu.edu)

ZoomThursday September 25, 2025 1:00pm - 3:00pm

This is the second part of a two-part tutorial on PyTorch. Be sure to also register for Part One on Tuesday, February 11, from 1 PM to 3 PM.

What to Expect: This session introduces PyTorch, a popular and versatile Python library for deep learning, optimized for acceleration processing using GPUs. You’ll gain hands-on experience building and training neural networks for binary classification.

Key Topics Covered:
  • Why PyTorch?
  • GPU acceleration using PyTorch Tensors
  • PyTorch Autograd for automatic differentiation
  • Working with Data
  • Datasets and Data Loaders in PyTorch
  • Building Neural Networks
  • Developing deep learning models for binary classification using PyTorch
Preparation:
  • Experience with Python programming, especially using Jupyter Notebook, is required.
  • Before the tutorial, ensure Python is installed on your machine. Detailed setup instructions and a conda environment file with the required packages will be shared.
  • If you plan to use your own computer, the conda environment must be installed and activated in advance.
Prerequisites:
  • Familiarity with Python NumPy library.
  • Basic understanding of machine learning and deep learning concepts and experience in using them.

Additional Recommendation: For those new to machine learning, consider attending the preceding tutorials on Machine Learning with Python Scikit-Learn to build foundational knowledge.

Get ready to dive into PyTorch and create powerful deep learning models!

IntermediateOpen-Source Generative AI with Hugging Face (Hands-on)NEW ICON

Instructor: Atish Kamble (akamble@bu.edu)

ZoomThursday October 2, 2025 1:00pm - 3:00pm

What to Expect

This tutorial introduces Hugging Face, the leading open-source platform where the machine learning community collaborates on models, datasets, and applications. Best known for its Transformers library for natural language processing, Hugging Face also provides a vibrant ecosystem for sharing, exploring, and deploying machine learning models.

You will gain hands-on experience navigating the Hugging Face platform, exploring models that meet specific criteria, and building tools using these models. By the end, you’ll understand how Hugging Face supports modern AI development and how to leverage its tools effectively in your own projects.

Key Topics Covered:
  • 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
  • Understanding the platform’s tools, libraries, and community features
  • Hands-on practice: selecting models, working with them in Python, and building simple applications
Preparation:
  • Prior experience with Python programming (especially Jupyter Notebooks) is required
  • Ensure Python is installed on your machine; detailed setup instructions and a conda environment file will be provided in advance
  • If you plan to use your own computer, install and activate the conda environment ahead of time
Prerequisites:
  • Familiarity with the NumPy library
  • Basic understanding of machine learning and deep learning concepts with some practical experience
Recommended Background:

If you are new to machine learning, consider attending the preceding tutorials on Machine Learning with Python (Scikit-Learn) and Deep Learning with PyTorch to build a solid foundation.

Get ready to explore Hugging Face, experiment with open-source models, and build your own AI-powered applications!

Data Analysis Tutorials

BeginnerIntroduction to MATLAB, Part One (Hands-on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSCMonday September 8, 2025 1:00pm - 3:00pm
MATLAB is an interpreted programming language. It 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. This tutorial presents an introduction via solving hands-on example problems; this motivates 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. Please also register for Part Two. No prior programming experience in any language is required to attend this course. If you do not have MATLAB installed on your home machine, please read and follow these instructions prior to attending the tutorial.

BeginnerIntroduction to MATLAB, Part Two (Hands-on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSCWednesday September 10, 2025 1:00pm - 3:00pm
MATLAB is an interpreted programming language. It 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. This tutorial presents an introduction via solving hands-on example problems; this motivates the syntax/tools in a “why” versus “what” way. Part Two introduces participants to basic features including file reading/writing, functions, and text processing. Please also register for Part One. No prior programming experience in any language is required to attend this course.

IntermediateNumerical Computing in Python (Hands-on)

Instructor: Brian Gregor (bgregor@bu.edu)

BSCTuesday September 16, 2025 10:00am - 12:00pm

Python is now widely used for numerical calculations and data analysis. This tutorial is an introduction primarily 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. This tutorial assumes familiarity with Python.

Prerequisite: If you are new to the Python programming language we strongly recommend that you also register for the "Introduction to Python" two-part tutorial.

If you do not have Python installed on your home machine, please read and follow these instructions prior to attending the tutorial.

BeginnerPython for Data Analysis (Hands-on)

Instructor: Brian Gregor (bgregor@bu.edu)

BSCThursday September 18, 2025 10:00am - 12:00pm

This tutorial will introduce the basics of Data Analysis with Python and its powerful libraries such as Pandas and Matplotlib.

What you will learn:
  •    Importing and Exporting the data
  •    Basic data processing, cleaning, and manipulation
  •    Basic inferential statistical analysis
  •    Data Visualization techniques
If you do not have Python installed on your home machine, please read and follow these instructions prior to attending the tutorial.

Domain Specific Tutorials

BeginnerIntroduction to GIS Theory (Lecture)

Instructor: Dennis Milechin (milechin@bu.edu)

ZoomMonday September 8, 2025 1:00pm - 3:00pm
This tutorial will introduce select core Geographic Information System (GIS) theory concepts that are utilized by the majority of GIS software and GIS libraries. The goal of this tutorial is to get you familiar with common GIS terminology and concepts that may not be clearly described when reading "How To" manuals of GIS software packages and GIS libraries. Topics that will be covered include:
  •    What is GIS?
  •    Geographic Coordinate Systems & Projections
  •    Spatial Data Models
  •    Data Layers
  •    Overview of spatial data files
  •    Example of a GIS workflow
  •    Overview of available GIS software and libraries
The content will be presented in lecture style and therefore no software needs to be installed prior to the tutorial.

BeginnerIntroduction to QGIS (Demonstration)

Instructor: Dennis Milechin (milechin@bu.edu)

BSCMonday September 15, 2025 1:00pm - 3:00pm

QGIS is an open source GIS desktop application that can be downloaded for free and it provides a collection of tools for managing, analyzing, and visualizing spatial data. This application has a resemblance to commercial GIS products ArcGIS Pro and ArcMap. In this session I will introduce you to the user interface of QGIS and go through simple workflows to get you started on using the software, such as importing data, symbolizing data, adding labels, and more.

Prerequisite: Introduction to GIS Theory

Related Tutorials:

If you do not have QGIS installed, please read and follow these instructions prior to attending the tutorial.

BeginnerIntroduction to ArcGIS Online Portal (Hands-on)

Instructor: Dennis Milechin (milechin@bu.edu)

ZoomMonday September 22, 2025 1:00pm - 3:00pm
The ArcGIS Online Web Portal is the starting point for any ArcGIS Online projects. It is a cloud based web application that allows you to upload and store your own GIS data, save web maps, StoryMaps, and other ArcGIS Online applications in a centralized location. You can control access permissions for your content and share completed work with the world, or restrict access to only your working group. It also provides tools to search GIS data that was published on the ArcGIS Online platform and makes it easy to import that data into your applications. In this tutorial, I will give a brief introduction on what is ArcGIS Online. Then we will log onto the web portal and I will explain the structure of the user interface and how to use the basic tools to manage your content, manage permissions, and search for data. In this tutorial we will not cover how to use specific applications (such as Web Viewer, StoryMaps, Dashboards, etc). No software installation is required; just an internet connection and an internet browser. You will need an ArcGIS Online account.

BeginnerIntroduction to OpenStreetMap (Hands-on)NEW ICON

Instructor: Dennis Milechin (milechin@bu.edu)

ZoomMonday September 29, 2025 1:00pm - 3:00pm
OpenStreetMap is a name you might have seen at the bottom corner of web maps as a copyright item, or you might have used a GIS tool that uses OpenStreetMap data, but what is OpenStreetMap? This tutorial will provide an overview of OpenStreetMap and show how it is used by companies and organizations for some applications. We will explore the data OpenStreetMap contains, learn how the data is structured, how to contribute information to OpenStreetMap, and what tools are available to access the data. This tutorial will be a mix of demonstrations and hands-on activities.