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.
You do need to have a laptop with a Mac, Linux or Windows operating system (not a tablet, Chromebook, etc) on which you have administrative privileges, as you need to pre-load specific software packages.
Today, Machine Learning and AI technologies are transforming science, but knowing how to get started can feel overwhelming. Our workshop introduces foundational concepts and methods of Machine Learning with practical examples and exercises.
Join CBW’s intensive 3-day hands-on workshop to learn, apply and assess machine learning techniques. Learn from our experts, network with peers and start your Machine Learning journey!
• Where: Halifax, Nova Scotia, Canada
• When: October 14-16, 2026
The Canadian Bioinformatics Workshops (CBW) offered through bioinformatics.ca have been teaching Bioinformatics for over 25 years, reaching over 4000 students through more than 200 workshops.
You will gain experience in:
• Applications and Limitations of Machine Learning and Deep Learning;
• Decision Trees and Random Forests;
• Artificial Neural Networks (ANNs);
• Fundamentals of clustering as an unsupervised machine learning method;
• Machine learning operations principles; and
• Applying these machine learning techniques for biomarker discovery, secondary structure prediction and more.
Only 30 seats are available, so reserve your seat today!
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 behavior.
Computational thinking is an essential skill for anyone wanting to learn to program computers and write code. This workshop will take 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 will practice techniques such as problem decomposition, abstraction, and algorithmic thinking. By the end of the session, you will have a stronger foundation for approaching programming tasks with confidence, setting the stage for future coding and data-driven learning.
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.
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.
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.
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.
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.
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 any previous knowledge of the tools that are presented or previous programming experience but intermediate level experience with a computer is highly recommended.
You do need to have a laptop with a Mac, Linux or Windows operating system (not a tablet, Chromebook, etc) on which you have administrative privileges, as you need to pre-load specific software packages.
