AI for Data & Analytics

Analysts and data teams learn to use AI to query, analyze, and explain data while validating quality. The course covers natural language to SQL and code, analysis and visualization, RAG over data, and quality and validation. Participants finish able to deliver faster insights they can stand behind.

What this course covers

The course runs across 14 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.

Introduction to AI-Assisted Data Analytics — Overview of how LLMs and generative AI transform the data analytics workflow, setting expectations for accuracy, speed, and the human-in-the-loop paradigm.
Natural Language to SQL: Basic Queries — Translating simple user queries into SELECT statements, filtering, and basic joins using AI assistants. Understanding how to prompt for specific schema structures.
Natural Language to SQL: Complex Queries & Optimization — Generating complex SQL involving aggregations, subqueries, CTEs, and window functions. Learning to prompt the AI to optimize slow-running queries.
AI-Powered Code Generation for Data Prep — Using AI to generate Python (Pandas) and R code for data cleaning, handling missing values, merging disparate datasets, and restructuring data tables.
Exploratory Data Analysis & Statistical Modeling with AI — Leveraging AI to suggest statistical tests, generate code for correlation analysis, and draft initial summaries of dataset distributions and anomalies.
Automated Data Visualization — Generating Matplotlib, Seaborn, or Plotly code via natural language. Iterating on chart aesthetics, labels, and formats to build presentation-ready assets.
Introduction to RAG (Retrieval-Augmented Generation) Over Data — Understanding the architecture of RAG systems. How LLMs connect to external databases, documentation, and metadata to answer contextual data questions.
Querying Unstructured & Semi-Structured Data with RAG — Hands-on practice using RAG tools to extract insights from PDFs, JSON logs, and text fields, combining them with structured database tables.
Validating AI-Generated Code and SQL — Techniques for spotting subtle hallucinations in SQL joins, verifying logic, running dry-runs, and using execution plans to validate AI output.
Data Quality Assurance & Guardrails — Using AI to write automated unit tests for data pipelines. Designing prompt-based guardrails to prevent SQL injection and unauthorized data access.
Handling Hallucinations & Discrepancies — A systematic approach to debugging when AI-generated analysis conflicts with known business logic or ground-truth data.
Synthesizing Insights & Automated Reporting — Using AI to translate complex data findings and visualizations into executive summaries, slide outlines, and narrative reports.

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 Communicating Data Insights, SQL Querying for BI, Data Validation, Working with AI Tools, Retrieval-Augmented Generation (RAG).

Communicating Data Insights
SQL Querying for BI
Data Validation
Working with AI Tools
Retrieval-Augmented Generation (RAG)

Who it is for

Analysts, data scientists, BI teams. The material is pitched at intermediate level, and takes roughly 180 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 Function: Data & Analytics 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 guided lab, code lab, branching scenario exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code 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.

See how this training works for the whole function on AI training for Data & Analytics.

Also in this track

AI for BI & Reporting — A practical course for BI teams: natural-language querying, AI-assisted dashboards, and automated report narratives. Learners ship a dashboard with a generated narrative layer they can defend.
AI for Data Science — A course for data scientists on using AI through the modeling workflow: exploration, features, experiments, and documentation. The emphasis is speed without sacrificing statistical rigor.
AI for Data Analysis: Essentials — Learners will be able to use AI for everyday analysis: formulas, cleanup, and takeaways. The course covers asking for a formula and testing it on a few rows, cleaning a messy column with clear instructions, and writing the two-line takeaway under every table. They leave analyzing faster while spot-checking every number by hand.
Data & Analytics with AI — Query, analyze, and explain data with AI

Frequently asked questions

How long does this course take?

It runs roughly 180 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 FUN-DAT.