IS&T RCS Fall 2026 Trainings

September 9 – October 1, 2026

Registration is open for the RCS Fall 2026 Tutorials. Please also be aware that we have lots of recordings and slides available from past tutorials by RCS staff and vendors.

  • 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.

The RCS tutorials cover concepts, techniques, and tools which researchers can use in their own computing environments. Many are designed to help you make effective use of the Boston University Shared Computing Cluster (SCC). The RCS staff can also deliver extra, or customized, tutorial sessions to your course, group, or lab. 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 Linux (Hands-on)

Instructor: Augustine Abaris (augustin@bu.edu)

BSCWednesday September 9, 2026 1:00pm - 3:00pm
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.

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

Instructor: Aaron Fuegi (aarondf@bu.edu)

BSCThursday September 10, 2026 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.

IntermediateIntermediate Usage of the SCC (Lecture)

Instructor: Katia Bulekova (ktrn@bu.edu)

BSCMonday September 14, 2026 1:00pm - 3:00pm
ZoomTuesday September 15, 2026 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 8, 2026 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 10, 2026 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.

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

Instructor: Brian Gregor (bgregor@bu.edu)

ZoomTuesday September 15, 2026 1:00pm - 3:00pm

This is the first part of a two-part tutorial series. Be sure to also register for Part Two 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: Brian Gregor (bgregor@bu.edu)

ZoomThursday September 17, 2026 1:00pm - 3:00pm

This is the second part of a two-part tutorial series. Be sure to also register for Part One.

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!

BeginnerNatural Language Processing Basics (LLMs Part 1) (Hands‐on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSCTuesday September 22, 2026 10:00am - 12:00pm
This is part one of a three part series but those wanting a brief introduction to Natural Language Processing (NLP) should feel free to attend just this session.   Human language/communication can be studied computationally through NLP. We'll explore the basics of NLP using Python and PyTorch; no prior machine learning experience is necessary, basic Python knowledge is helpful but not necessary. We will create a bigram character model and use it to generate random first names of people; we will build statistical and neural network implementations.

IntermediateIntermediate Language Models (LLMs Part 2) (Hands‐on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSCThursday September 24, 2026 10:00am - 1:00pm
Large Language Models (LLMs) first require successful "non-large" language models. We'll look at word embeddings, a technique for encoding words as vectors that capture their semantic meaning. We’ll examine the popular word2vec method and build/train a model to generate our own word embeddings. Finally we’ll explore how to perform linguistic operations using vector arithmetic with word2vec.

AdvancedGPT & Transformers (LLMs Part 3) (Hands‐on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSCTuesday September 29, 2026 1:00pm - 3:00pm
Training Large Language Models (LLMs) requires a large neural network, large data, and large compute. We will discuss these difficulties. We’ll look at the Transformer architecture in detail to develop a quantitative understanding of how it works and how specifically tools like ChatGPT, DeepSeek, Llama, etc. work. We will then use a pre-trained SentenceTransformer model to do a range of classification on real-world data.

AdvancedSpecial/Advanced Topics in ML (Hands-on)

Instructor: Josh Bevan (jbevan@bu.edu)

BSCMonday September 28, 2026 10:00am - 12:00pm

We will cover several topics, demonstrating how they are useful for applying ML techniques/tools to broader research. We will examine:

  1. "Whisper" for speech recognition and transcription
  2. Model Context Protocol (MCP) for expanding capabilities of AI models to use external data and tools

Topics covered by this tutorial in future semesters will change based on community needs and development of new techniques/tools.

IntermediateDeep Learning with PyTorch (Hands-on)

Instructor: Josh Bevan (jbevan@bu.edu)

ZoomThursday October 1, 2026 1:00pm - 3:00pm

What to Expect: This tutorial 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!

Data Analysis Tutorials

BeginnerPython for Data Analysis (Hands-on)

Instructor: Brian Gregor (bgregor@bu.edu)

BSCMonday September 21, 2026 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)

ZoomWednesday September 16, 2026 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 ArcGIS Online Portal (Hands-on)

Instructor: Dennis Milechin (milechin@bu.edu)

ZoomWednesday September 23, 2026 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 Nextflow on the SCC (Hands-on)NEW ICON

Instructor: Yun Shen (yshen16@bu.edu) Andy Rampersaud (aramp10@bu.edu)

ZoomThursday October 1, 2026 10:00am - 12:00pm

This tutorial will introduce Nextflow, a workflow language for creating scalable, portable, and reproducible pipelines. A Nextflow pipeline connects processing steps to transform input data into output results – useful for building a new pipeline, adapting one you inherited, or running a published pipeline such as those on nf-core. Attendees will learn the basics of Nextflow syntax and structure, build and run a simple pipeline, and learn how to run a Nextflow script/pipeline on BU's Shared Computing Cluster (SCC). The focus of this tutorial will be on Nextflow's use in bioinformatics applications, though the concepts covered broadly apply to other fields – all are welcome to attend. This tutorial is recommended for those with prior experience with use of the SCC, or attendance of our 'Introduction to BU's Shared Computing Cluster' tutorial.