Course title: Cloud Fundamentals

Delivery scope: Eleven ordered modules; schedule and workload agreed for each client engagement

Delivery format: Instructor-led learning combined with demonstrations, guided Azure practice, applied analysis, and an integrated cloud solution case

Intended audience: Professionals and learners who work with technical digital services, IT systems, infrastructure, or development and need practical cloud capability

Course description: This course develops the fundamental knowledge and applied judgment needed to understand, configure, and evaluate cloud solutions. Participants progress from cloud terminology and Microsoft Azure orientation to identity, networking, compute, security, operations, recovery, containers, and cloud strategy. The course is practical and decision-oriented. Participants use Azure-centred examples to examine how a cloud solution can meet organizational needs while balancing functionality, cost, security, supportability, recovery, and scalability.

Course title: Cybersecurity

Delivery scope: Eleven modules covering threats, defensive security, risk, and incident response. Adapted in pace and workload per client.

Delivery format: Instructor-led sessions with analysis, guided exercises, case work, and a security improvement project. Delivered on-site, remote, or blended. Intended audience: Professionals working with digital services, IT, or operations who need practical cybersecurity foundations.

Course description: his course provides a practical introduction to cybersecurity across modern digital environments. Participants learn to identify common threats, assess risks to digital assets, and apply foundational defensive measures, including identity security, monitoring, and incident response. The course emphasises structured, responsible security work, connecting threat awareness, vulnerability analysis, and risk prioritisation to real-world decisions. Through guided exercises and a security improvement project, participants develop the ability to analyse, communicate, and strengthen organisational resilience, including key ethical, legal, and governance considerations.

Course title: Python for AI Development

Delivery scope: Eight ordered modules; schedule and workload agreed for each client engagement

Delivery format: Instructor-led learning combined with demonstrations, guided practice, independent exercises, and an applied project

Intended audience: Participants who want to build practical foundations in Python, data work, and introductory AI development

Course description: This course gives participants a practical route from first Python programs to small, responsibly interpreted AI workflows. Participants learn to work with code and data, visualize patterns, train and evaluate introductory machine learning models, and understand a simple neural-network image-classification workflow through a guided TensorFlow and Keras demonstration. The course emphasizes understandable workflows and evidence-based model choices. Participants learn that model quality depends on a clear problem, appropriate data, careful evaluation, and awareness of limitations and responsible use.

Course title: RPA with AI Support

Delivery scope: Eight modules covering process analysis, RPA development, AI-supported automation, and value evaluation. Adapted in pace and workload per client.

Delivery format: Instructor-led sessions with demonstrations, guided practice, applied cases, and an automation project. Delivered on-site, remote, or blended.

Intended audience: Professionals working with digital business or support processes who want to automate workflows responsibly. No prior RPA or programming experience required.

Course description: This course provides a practical introduction to Robotic Process Automation (RPA) using UiPath, from identifying automation opportunities to building and evaluating workflows. Participants learn to analyse processes, design and implement automations, and ensure solutions are structured, traceable, and maintainable. The course incorporates a modern automation perspective by introducing AI-supported capabilities such as OCR, NLP, and LLMs. Participants explore when AI adds value and when simpler, deterministic automation is more appropriate. Through guided exercises and an applied project, they develop the ability to design end-to-end automation solutions, assess business value, and address key considerations around data protection, reliability, and responsible use.