Funding Tracker
2026-09-10
ACENET: free training sessions for faculty and students now open for registration
Introduction to High Performance Computing (HPC)
15 September, 1200-1330hrs Atlantic | 1230-1400hrs NL (online)
What is high performance computing (HPC) and what can it do for me? How can ACENET help? Used by researchers across many disciplines to tackle analyses too large or complex for a desktop, or to improve efficiency over a desktop, this session takes you through the preliminary stages of learning about HPC and computing clusters, and how to get started with this type of computing. It then reviews software packages available for applications, data analysis, software development and compiling code. Finally, you are introduced to the concept of parallel computing to achieve much faster results in analysis. This session is designed for those with no prior experience in HPC, and who are looking for an introduction and overview.
Introduction to the Linux Command Line
16 September, 1200-1330hrs Atlantic | 1230-1400hrs NL (online)
Linux is the terminal interface used to enable you to use the ACENET and the Digital Research Alliance of Canada (DRAC) high performance computing (HPC) clusters from your desktop. It's the tool you need to get your data on the clusters, run your programs, and get your data back. In this session, learn how to get started with Linux, how to create and navigate directories for your data, load files, manage your storage, run programs on the computing clusters, and set file permissions. This workshop is designed for those with no prior experience in working with a terminal interface.
Introduction to Shell Scripting
17 September, 1200-1330hrs Atlantic | 1230-1400hrs NL (online)
Shell scripting helps you save time, automate file management tasks, and better use the power of Linux. In this session, you learn how to use the command line to carry out repetitive tasks, extract information from files quickly, combine commands in powerful ways, about job scripts, shell variables and looping commands, and how to capture a workflow so you can re-use it easily. Save time, reduce errors, and use Linux more effectively. This workshop is designed for either new high performance computing (HPC) users who are familiar with working in a Linux environment, or for experienced users seeking to get more out of shell scripting. Participants are strongly encouraged to have a Digital Research Alliance of Canada account.
Job Scheduling with Slurm
18 September, 1200-1330hrs Atlantic | 1230-1400hrs NL (online)
The national systems use a job scheduler called “Slurm”. In this session you learn how Slurm works and how it allocates jobs, helping you to: minimize wait time by framing reasonable requests; ask for only the resources you need to improve efficiency; increase throughput; run more jobs simultaneously; and, troubleshoot and address crashes. This workshop is designed for either new high performance computing (HPC) users familiar with Linux and Shell Scripting, but who have not had experience using Slurm, or, for experienced users transitioning to Slurm or seeking to improve efficiency with the scheduler. Participants should have a Digital Research Alliance of Canada account.
Introductory Programming: Unix Shell, Git and Python
29 September and 1, 6, 8 October, 1300-1600hrs Atlantic | 1330-1630hrs NL (online)
This is a beginner level series that is hands-on, covering the fundamentals of Unix Shell, Version Control with Git and Python. This includes data types, conditional statements, loops and functions, as well as program design, version control, data management, and task automation. Participants are encouraged to help one another and to apply what they have learned to their own research problems. The goal is to teach the practical knowledge needed to start programming, debugging and using Python in everyday tasks. You do not need to have previous knowledge of the tools that are presented, or previous programming experience, but intermediate level experience with a computer is highly recommended.
Introduction to Computational Thinking
7 October, 1300-1600hrs NL (in-person, Memorial University)
Note that there is no online option.
Computational thinking is an essential skill for anyone wanting to learn to program computers and write code. This workshop takes you through the steps involved in computational thinking, learning how to break down complex problems into smaller parts, identify patterns, and design logical solutions that a computer can execute. Through hands-on activities, you practice techniques such as problem decomposition, abstraction, and algorithmic thinking. By the end of the session, you have a stronger foundation for approaching programming tasks with confidence, setting the stage for future coding and data-driven learning. This session is designed for beginners who are curious about programming but may not know where to start. It’s especially valuable for those exploring computer science or data science for the first time, or for people in non-technical fields who want to build problem-solving and coding skills.
Microcredential in Practical Foundations for Data Analytics
20 October to 10 November, Tuesdays and Thursdays, 1400-1700hrs Atlantic | 1430-1730hrs NL (online)
Note that space is limited and participants must submit an application.
This microcredential provides a comprehensive introduction to the essential tools and techniques required for modern computational data analysis. The program combines classroom and self-study learning to build foundational skills in Linux, Python, version control with Git, cybersecurity, and high performance computing (HPC). Participants gain hands-on experience with essential computational tools and methods and develop practical skills. Whether you are beginning to integrate computational tools into your workflow or seek to expand your knowledge, this microcredential offers a structured pathway to becoming proficient in key areas of data analytics. Participants need basic math skills and intermediate experience with computers, including working with documents and spreadsheets.
Using Spreadsheets to Organize Data
28 October, 1300-1600hrs Atlantic | 1330-1630hrs NL (online)
We use spreadsheet programs for entering, organizing, subsetting, and sorting data, for generating statistics and plots, and more. This course shows you some best practices that will result in fewer mistakes, greater reproducibility, and easier use of other software tools with data exported from spreadsheets. Learn how to: decide whether a spreadsheet or a database is the more suitable tool for a project; reorganize and reformat a spreadsheet to avoid common problems; parse and format dates using spreadsheet functions; detect and correct some kinds of data errors in spreadsheets; export spreadsheet data in portable formats; and, use a pivot table to summarize data in different ways. Participants should be comfortable using a computer and navigating files and folders. No prior programming experience is necessary.
Introduction to Machine Learning
2, 9, 16 November, 1200-1500hrs Atlantic | 1230-1530 NL (online)
These hands-on sessions guide learners through the end-to-end machine learning lifecycle using Python and Scikit-learn. Participants evaluate when to apply machine learning to real-world challenges and implement initial baseline approaches for straightforward datasets. Through interactive coding exercises, learners import data, preprocess features, and build, train and evaluate fundamental models. The curriculum contrasts key supervised learning algorithms including linear and logistic regression, decision trees, and random forests while introducing unsupervised methods like K-Means clustering to discover latent structures within unlabeled data. Learners quantify model performance using regression metrics like R2 and mean squared error alongside classification tools such as accuracy and confusion matrices.
Data Cleaning with OpenRefine
3 November, 1000-1300hrs Atlantic | 1030-1330hrs NL (online)
Researchers and librarians often deal with “messy” data. This could include inconsistent interview transcripts, poorly formatted longitudinal datasets, or standardizing names for an authority file. Preparing this data for analysis is a critical step that requires the same level of rigor and reproducibility as the analysis itself. This workshop introduces OpenRefine, a powerful, free, and open-source tool for cleaning, normalizing, and transforming research data. participants work with a sample dataset to learn how to efficiently handle common data issues, such as automating data standardization, identifying clusters of similar entries, and transforming complex strings into structured information without the need for advanced programming. Unlike manual editing in Excel, OpenRefine records every step of your process, ensuring your data cleaning is transparent and fully reproducible. Participants should be comfortable using a computer and have a general understanding of spreadsheets. No prior programming experience is necessary.
Introduction to R for Reproducible Scientific Analysis
17, 19 November, 1200-1500hrs Atlantic | 1230-1530hrs NL (online)
This hands-on, beginner level series covers the fundamentals of R, including data types, functions, importing, manipulating and analyzing data as well as program design, data management, and task automation. Participants are encouraged to help one another and to apply what they have learned to their own research problems. The goal is to teach the practical knowledge needed to start programming, debugging and using R in everyday tasks. You do not need any previous knowledge of the tools that are presented, or previous programming experience, but intermediate level experience with a computer is highly recommended.
Visualization with R
24 November, 1200-1400hrs Atlantic | 1230-1430 NL (online)
When managing large datasets, visual representation is essential for uncovering hidden patterns and communicating insights. This session focuses on transitioning from raw numbers to impactful graphics — such as histograms, scatter plots, and box plots — using the powerful R programming language. While basic R knowledge is helpful, this workshop is designed to be accessible to all. We move beyond simple charting to explore the "Grammar of Graphics" approach, teaching you how to build visualizations layer-by-layer. Participants should be familiar with R (variables, vectors, and data frames).
Introduction to Large Language Models (LLMs)
3 December, 1200-1500hrs Atlantic | 1230-1530hrs NL (online)
This comprehensive, non-coding session provides an architectural walkthrough of Large Language Models (LLMs), bridging the gap between high-level AI concepts and practical model utilization. The session traces the historical evolution of Natural Language Processing (NLP), tracking how early statistical models (N-grams, Bag-of-Words) and sequential deep learning networks transitioned into modern self-attention paradigms. Participants examine the inner workings of the Transformer architecture, exploring tokenization, embeddings, multi-head self-attention, and the structural trade-offs between encoder-only, decoder-only, and encoder-decoder designs, while analyzing how context window limits dictate memory consumption and computational complexity. Beyond core mechanics, the session evaluates the current LLM ecosystem through model benchmarks, comparing leading families (OpenAI, Anthropic, Google, Meta, and DeepSeek) across parameters like reasoning, cost efficiency, multimodal capabilities, and alignment. Finally, through guided visual demonstrations and concrete examples, participants discover how to access these models via proprietary APIs and open-source platforms (such as Hugging Face), identify key failure modes (hallucinations, bias, prompt injection), and learn some applied prompt engineering patterns to reliably direct model behaviour.