Technical Track
AI Engineering Foundations (Python, Data & Automation)
Hands-on bootcamp for engineers and analysts who need to build reliable AI pipelines. Master Python tooling, data wrangling, visualisation and introductory ML to accelerate automation projects.
AI Engineering Foundations – Python for AI & Automation
A technical bootcamp that teaches the Python ecosystem, modern data pipelines and automation patterns. Every learner leaves with reusable notebooks, visualisation dashboards and starter ML projects.
Programme Objectives
- Master Python syntax, data structures and productivity tips.
- Prepare, clean and transform datasets with NumPy & Pandas.
- Create professional-grade visualisations using Matplotlib & Seaborn.
- Automate workflows with notebooks, scripts and API calls.
- Understand the foundations of machine learning and evaluate simple models.
Target Audience
- Junior developers or analysts moving into AI engineering roles.
- Data analysts and BI specialists who need stronger Python skills.
- Automation engineers, citizen data scientists and consultants.
- Technical project managers overseeing AI or analytics tracks.
Prerequisites
- Basic understanding of information systems or scripting concepts.
- Comfort working with spreadsheets or structured datasets.
Format & Learning Approach
- Duration: 3 days (21 hours) – optional condensed 2-day format for experienced teams.
- Live instructor-led sessions with paired programming, debugging clinics and lab challenges.
- Bring-your-own-data segment to practice on your organisation’s context (optional).
Programme Overview – Technical Journey
Module 1 – Python Foundations & Engineering Environment
Set up Anaconda/Jupyter, review Python syntax, control flow, functions and best practices (PEP 8, virtual environments, package management).
Module 2 – Data Manipulation with NumPy & Pandas
Load, clean and reshape datasets, handle missing values, join datasets and build reusable transformation pipelines.
Module 3 – Visualisation & Insight Delivery
Create dashboards and data stories using Matplotlib & Seaborn (heatmaps, box plots, time-series). Emphasis on readability and stakeholder-ready charts.
Module 4 – Automation & Integration Patterns
Automate notebooks, schedule scripts, call APIs, interact with files/cloud storage and package reusable utilities.
Module 5 – Introduction to Machine Learning & Assessment & certification
Discover Scikit-learn workflow, build baseline models, evaluate metrics, avoid common pitfalls and prepare for advanced AI engineering tracks.
Deliverable: personal automation project and starter ML notebook ready to extend.
Outcomes & Key Benefits
- Operational Python skills applicable to data prep, automation and AI pilots.
- Reusable notebook templates and visualisation libraries.
- Confidence to collaborate with data scientists and MLOps teams.
- Foundations to progress towards AI Engineer or GenAI implementation tracks.
Teaching Methods
- Instructor-led demos followed by pair-programming labs.
- Case studies drawn from real sales, finance or IoT datasets.
- Office hours and code reviews to reinforce best practices.
Assessment & Certification
- Continuous assessment via lab challenges and small quizzes.
- Final review of the automation project or ML notebook.
- Certificate of completion validating technical competencies.
Standards & references
- PEP 8 – Python coding standards for readability and maintainability.
- ISO/IEC 27001 – guidance for protecting datasets handled during labs.
- MLOps & reproducibility best practices – notebook versioning, documentation and audit trails.