Data and AI Literacy: Live Online Course

Program Experience

This course moves through six sessions that follow the full arc of modern data and AI. You start with what data actually is, where it originates, and what makes it trustworthy, then move into how organizations process and analyze data at scale using cloud infrastructure, pipelines, and distributed compute.

From there, you build an intuition for machine learning by examining supervised, unsupervised, and reinforcement learning through real-world case studies drawn from the instructor's industry experience. The course then shifts into generative AI, covering how transformer-based large language models work, what retrieval pipelines and context engineering look like in practice, and why token economics are becoming a budget-level concern.

The final sessions focus on agentic systems and the business patterns shaping AI adoption. You examine what happens when AI moves from answering questions to taking actions, how governance and security shift when models can use tools, and why many organizations adopt AI without seeing returns. Three frameworks thread the course together: OBAS for data governance, the Agentic Spectrum for AI autonomy, and the AI System Model for the whole.

A recurring theme runs through the course: AI doesn't fix broken processes. It scales them.

Course Info

Benefits

Topics

Who Should Attend

Schedule

FAQ

By the end of this course, you will be able to:

Walk into a room where someone says "we need a data strategy" and know whether they're talking about storage, governance, quality, or all three
Look at a dashboard and spot the difference between a metric that tells you something and one that just looks impressive
Follow a conversation about machine learning models, training data, and production deployment without needing a translator
Read a vendor pitch about context integration methods like Cache Augmented Generation (CAG), Retrieval Augmented Generation (RAG) pipelines, or context windows and ask the questions that separate a real capability from a buzzword
Evaluate whether an AI agent proposal is solving a problem worth automating or just adding complexity to a process that doesn't need it
Engage your CTO or data team and talk about token costs, deployment gates, model drift, and hiring needs in terms that move decisions forward
Recognize when an AI proposal is being layered onto a broken process and would just scale dysfunction rather than fix it
Speak with familiarity about the privacy, governance, and sector-specific frameworks shaping how organizations build, deploy, and audit AI systems

Session 1 - The Data Around Us

Structured, unstructured, and semi-structured data: what it is, where it comes from, and how it has changed over two decades
Data quality dimensions (completeness, accuracy, consistency, timeliness) and storage options from flat files to cloud-based data lakes
Data security, access control, and the OBAS framework (Observable, Bounded, Accountable, Secure) for governance

Session 2 - Making Sense of Data

Four types of analytics (descriptive, diagnostic, predictive, prescriptive) and how dashboards, KPIs, and reporting surface insight
Big data, the 3Vs of data (volume, velocity, and variety), distributed compute with tools like Apache Spark, and data pipelines and ETL
Real-time vs batch processing tradeoffs and how cloud providers changed the economics of storage and compute

Session 3 - Machine Learning

Supervised, unsupervised, and reinforcement learning explained at a conceptual level with real-world examples

ML case studies from industry showing how models move from concept to production
Neural networks, deep learning, and computer vision applications in medical imaging, manufacturing, and autonomous systems

Session 4 - Generative AI

The transformer architecture, the attention mechanism, and the frontier labs building foundation models
Retrieval pipelines (RAG, CAG), fine-tuning for regulated industries, and context engineering within token limits
Tokenomics, AI FinOps, and guardrails for keeping LLM outputs predictable, compliant, and aligned with business rules

Session 5 - Agents and Agentic Systems

What AI agents are, how they differ from simple interactions, and evaluating easy vs hard agentic tasks
The Agentic Spectrum covering seven levels of autonomous behavior, and the AI System Model (data, model, application, governance)
AI security (prompt injection, data exfiltration, unauthorized tool use) and governance frameworks for responsible deployment

Session 6 - Themes in Today's AI Systems

Token management as a business priority, creating repeatable AI coding infrastructure, and evaluating ML and AI talent hires
Pre and post deployment gates: testing, validation, monitoring, model drift, and shutdown criteria
Responsible AI, — privacy regulations (GDPR, CCPA), AI-specific frameworks (EU AI Act, NIST AI RMF, ISO 42001), and sector-specific guidance for financial services (SR 11-7, FINRA, FS RMF)
Common AI failure patterns: automating broken processes, elusive ROI and benefit realization, and the rise of low-quality AI-generated content ("AI slop," "AI junk food") and why it erodes trust in AI-produced work

This course is built for business leaders, managers, analysts, and professionals who work alongside technical teams or make decisions involving data and AI. No programming or data science background is required. You should attend if you want to build fluency in data and AI concepts so you can ask better questions, evaluate proposals, and participate in technical conversations with confidence. The course is also a fit for anyone moving into a role that touches AI strategy, product development, or technology procurement.

Course

Duration

Live Online (via Zoom)

Data and AI Literacy 2.0

9 Hours

On the following Thursdays:

August 13, 20, 27,
September 3, 10, 17, 2026

2:00 PM - 3:30 PM Pacific Time

Who is this course for?
This course is for working professionals who want a practical understanding of data and AI without needing a technical background. It fits leaders, managers, analysts, and anyone whose work intersects with data-driven decisions or AI initiatives.

Do I need programming experience?
No. The course is entirely conceptual and case-study driven. You will not write any code. All technical concepts are explained at a level accessible to non-technical participants.

What format is the course delivered in?
The course is delivered live online via Zoom across six sessions totaling 9 hours. Sessions are instructor-led with discussion and real-world examples throughout.

Will I learn how to build machine learning models?
This course focuses on literacy, not implementation. You will learn how ML and AI systems work at a conceptual level so you can evaluate proposals, ask informed questions, and contribute to strategy discussions.

Does the course cover generative AI and tools like ChatGPT?
Yes. Session 4 covers how large language models work, including the transformer architecture, retrieval augmented generation, context engineering, and the cost structure behind token-based pricing. Session 5 extends into agentic AI systems that take actions and use tools.

What is the OBAS framework?
OBAS stands for Observable, Bounded, Accountable, Secure. It is a practical framework introduced in Session 1 for evaluating how organizations manage and govern their data.

What is the AI System Model?
The AI System Model is a four-layer framework (data, model, application, governance) introduced in Session 5. It connects every concept covered across the course into a single view of how AI systems work as a whole.

Will I receive a certificate?
Participants who complete all six sessions receive a Caltech CTME Letter of Completion and 0.9 CEUs, suitable for sharing with employers, adding to LinkedIn, and submitting toward continuing education requirements.

Does this course cover AI regulations and risk-management frameworks?
Yes. Session 6 surveys the regulatory landscape shaping AI deployment, including privacy regulations (GDPR, CCPA), AI-specific frameworks (EU AI Act, NIST AI RMF, ISO 42001), and sector-specific guidance for financial services (SR 11-7, FINRA, FS RMF). The course is designed for literacy rather than compliance certification — participants leave able to recognize which frameworks apply to their context, what each is designed to address, and how to engage productively with their organization's compliance and risk teams.

Does this course help fulfill EU AI Act Article 4 AI literacy obligations?
Article 4 of the EU AI Act, in force since February 2025, requires providers and deployers of AI systems to ensure staff have a sufficient level of AI literacy. This course delivers the kind of conceptual fluency Article 4 describes — what AI systems are, their capabilities and risks, and how to evaluate them critically. Formal compliance also involves organizational steps such as documentation, role-tailored coverage, and assessment, which sit outside the course itself. Corporate enrollees may be able use this course as a foundational layer of an internal AI literacy program.

Data and AI Literacy

A practical foundation in data, machine learning, and generative AI