Open learning resources

From data to AI to its responsible deployment.

A guide to practical data science, machine learning foundations, and responsible AI, organized by learner goals.

Start here

Choose by what you need to do next

The same materials can serve different audiences: first-time data learners, technically curious students, leaders evaluating AI systems, and public-interest teams trying to deploy technology responsibly.

Practical Data Science

I’m new to data science

Begin with Practical Data Science: Python, notebooks, tabular data, cleaning, visualization, statistics, uncertainty, and first ML models.

View path ↓

Machine Learning Foundations

I want to understand machine-learning models

Use CSCI 3151 to move from learning paradigms and evaluation to kernels, neural networks, representation learning, and transformers.

View path ↓

Responsible AI

I make decisions about AI systems

Start with Responsible AI by Design: problem framing, ethical principles, fairness, explainability, governance, privacy, law, procurement, and culture.

View path ↓

Practical Data Science + Machine Learning Foundations + Responsible AI

I work in health, public service, or civic technology

Combine data literacy from CSCI 1109, model literacy from CSCI 3151, and deployment/governance literacy from Responsible AI.

View path ↓

Practical Data Science + Machine Learning Foundations

I need a bridge into the math

Use the math, descriptive statistics, probability, and linear-algebra portions of CSCI 1109 now. A future math refresher can be added as another resource card without changing the page design.

View path ↓

Courses and catalogs

Three ways into data, ML, and responsible AI

Each card links to the existing course site under Teaching. The summaries here are intentionally static and searchable, while the detailed materials remain in the original interactive course pages.

Course

CSCI 1109

Practical Data Science

Current public course · Introductory

Start here to learn how real-world questions become datasets, analyses, visualizations, models, and decisions.

Best for: Learners starting with data science, Python, tabular data, and evidence-based decisions.

Work with Python notebooks, pandas, NumPy, Matplotlib, scikit-learn, and NetworkX.
Move from messy data to visualization, inference, causal thinking, and first machine-learning models.
Build habits around cleaning, testing, evaluation, fairness, privacy, and communication.

60+ modules

11 clusters

Intro → core level

PythonpandasNumPydata cleaningvisualizationstatisticscausal thinkingscikit-learnregressionclassificationclusteringNetworkX

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Course

CSCI 3151

Foundations of Machine Learning

Current public course · Core undergraduate

Use this path to understand how machine-learning models are trained, evaluated, regularized, generalized, and connected to modern deep-learning architectures.

Best for: Students and self-directed learners who know some programming and want the conceptual machinery behind modern ML.

Develop a vocabulary for supervised, unsupervised, semi-supervised, and self-supervised learning.
Study likelihood, EM, model evaluation, bias–variance, regularization, optimization, kernels, and SVMs.
Connect foundational ideas to neural networks, CNNs, RNNs, transformers, embeddings, and representation learning.

60+ modules

4 assignments

Core ML level

ML paradigmssupervised learningunsupervised learningMLEEMevaluationbias–varianceregularizationoptimizationkernelsSVMsPCA

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Professional course

Microcredential

Responsible AI by Design

Public course materials · Professional / leadership

Use this path to ask better questions before AI systems are procured, adopted, governed, or communicated to affected people.

Best for: Business leaders, managers, product owners, procurement leads, compliance professionals, and public-sector decision makers.

Translate responsible AI principles into practical questions for leadership, procurement, governance, and deployment.
Evaluate bias, fairness, explainability, accountability, privacy, legal readiness, vendor risk, and organizational culture.
Build toward an AI Leadership Action Plan with owners, evidence artifacts, monitoring, escalation, and communication steps.

10 modules

~12 hours

Leaders audience

AI literacyethicsfairnessbias mitigationexplainabilitygovernanceaccountabilityprivacylawprocurementvendor riskculture

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Learning pathways

Suggested routes through the material

These paths do not prescribe a semester schedule. They describe useful sequences for self-study, professional development, or public-interest AI work.

Practical Data Science

I’m new to data science

Begin with Practical Data Science: Python, notebooks, tabular data, cleaning, visualization, statistics, uncertainty, and first ML models.

Next step

Open CSCI 1109 and start with Orientation & Python Foundations.

Machine Learning Foundations

I want to understand machine-learning models

Use CSCI 3151 to move from learning paradigms and evaluation to kernels, neural networks, representation learning, and transformers.

Next step

Start with ML Paradigms, then use the topic index when you need a specific concept.

Responsible AI

I make decisions about AI systems

Start with Responsible AI by Design: problem framing, ethical principles, fairness, explainability, governance, privacy, law, procurement, and culture.

Next step

Use each module as a checklist for decisions, vendors, policies, or leadership conversations.

Practical Data Science → Machine Learning Foundations → Responsible AI

I work in health, public service, or civic technology

Combine data literacy from CSCI 1109, model literacy from CSCI 3151, and deployment/governance literacy from Responsible AI.

Next step

Follow the full arc: evidence → model behaviour → governance and public accountability.

Practical Data Science → Machine Learning Foundations

I need a bridge into the math

Use the math, descriptive statistics, probability, and linear-algebra portions of CSCI 1109 now. A future math refresher can be added as another resource card without changing the page design.

Next step

Look for vectors, matrices, descriptive statistics, distributions, sampling, and bootstrapping.

Topic index

Browse by concept instead of course code

The topics below can be cross-cutting.

Data skills

PythonnotebookspandasNumPydata cleaningpipelinesvisualizationNetworkX

Statistics and evidence

descriptive statisticsdistributionssamplingbootstrappingp-valuesA/B testingcausal thinkingconfounding

Machine learning foundations

train/validation/test splitsbaselinesevaluation metricsregressionclassificationclusteringregularizationoptimizationkernelsSVMs

Modern AI

neural networksembeddingsCNNsRNNsattentiontransformersself-supervised learningsemi-supervised learning

Responsible AI

fairnessbias auditsprivacyexplainabilityaccountabilitygovernancelawprocurementvendor riskculture

From data to AI to its responsible deployment.

Choose by what you need to do next

I’m new to data science

I want to understand machine-learning models

I make decisions about AI systems

I work in health, public service, or civic technology

I need a bridge into the math

Three ways into data, ML, and responsible AI

Practical Data Science

Foundations of Machine Learning

Responsible AI by Design

Suggested routes through the material

I’m new to data science

I want to understand machine-learning models

I make decisions about AI systems

I work in health, public service, or civic technology

I need a bridge into the math

Browse by concept instead of course code

Data skills

Statistics and evidence

Machine learning foundations

Modern AI

Responsible AI

What each resource covers

Practical Data Science

Orientation & Python Foundations

Tabular Data with pandas

Cleaning & Preparation (Pipelines)

Math & Descriptive Stats

Visualization & Storytelling

Probability, Testing & Causal Thinking

Intro to ML: Supervised Learning

Regression & Classification

Unsupervised & Structure in Data

Ethics, Privacy & Responsible DS

Communication & Synthesis

Machine Learning Foundations

Intro to ML Paradigms

Supervised & Unsupervised Learning

Maximum Likelihood Estimation & EM

Model Evaluation & Bias–Variance

Regularization & Optimization Basics

Kernel Methods

Feature Engineering & Data Augmentation

Dimensionality Reduction

Neural Networks & Backpropagation

Training Stability

Generalization Techniques

Representation Learning & Embeddings

Convolutional Neural Networks

Recurrent Neural Networks

Transformers

Self-Supervised & Semi-Supervised Learning

Course Review & Synthesis

Responsible AI

AI Foundations and Ethical Principles

Fairness, Transparency, and Technical Controls

Governance, Privacy, Law, and Procurement

Culture, Leadership, and Capstone