Data Foundations for AI

Learners understand the data work that makes AI useful and trustworthy. The course covers data quality, structured versus unstructured data, labeling, pipelines, and privacy and governance of data. Participants finish able to prepare and reason about the data behind AI.

What this course covers

The course runs across 9 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Foundations of AI Data Strategy — Understand why data quality, volume, and diversity are critical to AI performance, and explore the lifecycle of data in AI/ML systems.
Processing Structured vs. Unstructured Data — Learn how to ingest and parse structured relational data alongside unstructured text and media, preparing both for model preprocessing.
Profiling and Assessing Data Quality — Programmatically detect missing values, duplicates, outliers, and statistical anomalies in training datasets using profiling tools.
Data Cleansing and Transformation — Implement data imputation, normalization, and feature encoding techniques directly in code to prepare clean inputs for AI models.
Designing and Managing Data Labeling Workflows — Establish annotation taxonomies, configure human-in-the-loop labeling tasks, and evaluate annotator consensus and label quality.
Architecting Scalable Data Pipelines — Design and build robust ETL/ELT pipelines that ingest, transform, and store high-volume data optimized for AI workloads.
Automating and Monitoring Pipeline Orchestration — Write code to schedule pipeline jobs, monitor execution health, and validate schemas to prevent pipeline breaks and data drift.
Data Privacy, Security, and Governance — Implement data masking, access controls, and lineage tracking to comply with global privacy standards while maintaining data utility.
Identifying and Mitigating Bias in Training Data — Analyze datasets for underrepresentation or historical bias, and apply balancing algorithms to ensure fair and trustworthy AI outcomes.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to Data Governance for AI, Data for AI, Data Privacy, Data Quality Management, Data Labeling & Annotation.

Data Governance for AI
Data for AI
Data Privacy
Data Quality Management
Data Labeling & Annotation

Who it is for

Analysts, builders, data teams. The material is pitched at intermediate level, and takes roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Category: Data Foundations track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including code lab, guided lab exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a lab assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Also in this track

Data & Analytics with AI — Query, analyze, and explain data with AI
Become an AI Builder — Technical foundation to build AI applications

Frequently asked questions

How long does this course take?

It runs roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code CAT-DATA.