Development & technology · Data Science & Machine Learning
AI training for Data Scientists and ML Engineers
Data science and machine learning thrive on moving quickly between exploration, modelling and production. A great deal of time goes into data preparation with pandas or Polars, feature engineering, visualisation, hyperparameter experiments and finally turning notebook code into maintainable pipelines. AI coding assistants and agents speed up every one of these steps: they write exploration and plotting code, suggest validation strategies, refactor notebooks into tested Python modules, build scikit-learn or PyTorch training scripts and set up experiment tracking with MLflow. Methodological rigour is essential, though, because plausible-looking code can hide data leakage or misleading metrics. This course teaches you to use AI productively while safeguarding the statistical quality of your results.
Who is this course for?
- Data scientists in business units and analytics teams
- ML engineers responsible for training and deployment
- Data analysts moving into Python and machine learning
- Researchers and developers with notebook-heavy workflows
- MLOps teams running models in production
Typical challenges we address
- Messy notebooks that are hard to reproduce and put into production
- Time-consuming data preparation and feature engineering
- Methodological pitfalls such as data leakage, unsuitable metrics or flawed validation
- Lack of traceability for experiments, models and data versions
Practical scenarios from your field
Exploratory data analysis
The assistant writes code for data profiles, distributions, correlations and missing values with pandas or Polars, produces meaningful visualisations and summarises anomalies as hypotheses for modelling.
Turning notebooks into pipelines
The agent refactors notebook code into Python modules with functions, configuration and pytest tests, removes hidden state and makes training and inference reproducible. Results become reliably comparable.
Model training and validation
The AI builds scikit-learn pipelines or PyTorch training loops with proper cross-validation, suitable metrics and baselines, and actively flags risks such as data leakage or class imbalance.
Experiment tracking and MLOps
The assistant sets up MLflow or similar tools for parameters, metrics and artefacts, versions models and creates training and deployment workflows for the CI/CD pipeline.
Explaining and auditing models
Using SHAP, permutation importance or partial dependence plots, the AI produces explanations of model decisions as well as analyses of fairness and robustness across relevant subgroups.
Evaluating LLM and RAG prototypes
The AI helps build evaluation datasets, automated scoring metrics and comparison experiments for prompt variants, embedding models or retrieval strategies. Quality, cost and latency of variants can be compared objectively.
Possible hands-on projects
Churn prediction with explanations
A complete project from data preparation through a gradient boosting model to a small Streamlit app displaying predictions and SHAP explanations.
Model monitoring script
A script that compares data and prediction distributions over time, detects drift and produces a compact report with charts. The team is notified automatically when anomalies appear.
Evaluation harness for a RAG chatbot
A test set-up with a question set, reference answers and automated scoring that compares different retrieval and prompt configurations. Results are summarised per configuration in a clear table.
All 12 courses for Data Scientists and ML Engineers
Vibe & agentic coding (12)
- Claude Code for Data Scientists and ML Engineers
- Vibe Coding for Data Scientists and ML Engineers
- Agentic Coding for Data Scientists and ML Engineers
- Cursor for Data Scientists and ML Engineers
- GitHub Copilot for Data Scientists and ML Engineers
- OpenAI Codex for Data Scientists and ML Engineers
- Windsurf for Data Scientists and ML Engineers
- Lovable for Data Scientists and ML Engineers
- Replit Agent for Data Scientists and ML Engineers
- Model Context Protocol (MCP) for Data Scientists and ML Engineers
- AI Agent Development for Data Scientists and ML Engineers
- RAG & LLM Applications for Data Scientists and ML Engineers
Data protection & compliance
Training and analysis data often contains personal information, so use schemas, aggregates or synthetic data in prompts and observe GDPR principles such as purpose limitation. For high-risk applications, the EU AI Act sets requirements on data quality, documentation and transparency, and the licences of datasets, pre-trained models and Python packages must also be checked.
Prerequisites
Good Python skills, experience with pandas or Polars and scikit-learn, and a grounding in statistics and machine learning.
Frequently asked questions
Can I trust AI on statistical questions?
AI assistants know common methods very well but occasionally make methodological mistakes, for example in splitting training and test data or choosing metrics. On the course you learn to ask the AI explicitly about assumptions and risks, check results against baselines and sanity checks, and keep professional accountability firmly with yourself.
Does AI-assisted coding work in Jupyter notebooks?
Yes. Many assistants support notebooks directly, and agents can also read, execute and convert notebooks into modules. We show when notebooks are ideal for exploration and how to make the transition to versioned, tested code efficiently with AI support. The two approaches complement each other.
Request without obligation
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