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.

What this course covers

The course runs across 13 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 Science Workflows — Understanding the integration of LLMs and generative AI tools into the modern data science lifecycle while maintaining analytical ownership.
Rapid Exploratory Data Analysis (EDA) with GenAI — Using AI assistants to quickly profile complex datasets, generate visualization code, and identify anomalous data patterns.
AI-Driven Feature Engineering & Generation — Leveraging LLMs to suggest domain-specific feature transformations and write scalable pandas/PySpark feature extraction code.
Maintaining Rigor: Preventing Data Leakage in Feature Work — Applying strict statistical guardrails to AI-generated features to prevent target leakage, overfitting, and training-serving skew.
Accelerated Model Baseline Generation — Prompting for and refining boilerplate model training scripts across multiple frameworks (Scikit-Learn, XGBoost, PyTorch).
AI-Assisted Hyperparameter Tuning & Optimization — Using AI to suggest optimal search spaces and implement advanced tuning strategies like Bayesian optimization.
Rigorous Experiment Design & Hypothesis Testing — Designing statistically sound A/B tests, power analyses, and control group selections with AI-guided experimental frameworks.
Robust Model Evaluation and Validation Loops — Implementing rigorous cross-validation, bootstrapping, and residual analysis scripts with AI assistance.
Debugging and Optimizing Machine Learning Code — Using AI assistants to identify logical errors, memory leaks, and performance bottlenecks in training pipelines.
Automating Model Documentation and Metadata — Generating comprehensive markdown documentation, model cards, and reproducibility records directly from code assets.
Model Interpretability: SHAP and LIME Integration — Accelerating the implementation of explainable AI (XAI) frameworks using AI-generated plotting and interpretation code.
Unit Testing for Machine Learning Pipelines — Writing robust unit tests for data schemas, transformation logic, and model outputs using AI-assisted testing suites.

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.

Who it is for

Data scientists. The material is pitched at advanced 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 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.
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 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.
AI for Data Analysis Pathway — Grow from everyday analysis habits into AI-assisted data science workflows.

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-DS.