Data Wrangling & Exploratory Data Analysis with Python

Data wrangling and exploratory data analysis (EDA) are essential steps in any data science or analysis project. This four-day course is designed for beginners and provides a comprehensive foundation in data preparation and exploration using Python. Participants will learn to clean, transform, and gain insights from real-world datasets, making them well-equipped for data-driven decision-making.

Business analysts play a critical role in bridging business needs, data, and technology. As organisations generate increasing volumes of data and face faster decision cycles, Artificial Intelligence (AI) is becoming an essential capability for modern business analysis.

Advances in Machine Learning, Large Language Models (LLMs), and Generative AI enable business analysts to analyse information more efficiently, uncover patterns, generate insights, document requirements, and communicate recommendations with greater clarity and speed. AI tools can support analysts across the entire analysis lifecycle—from problem definition and stakeholder engagement to data analysis, reporting, and strategic decision support.

This course provides a practical, business-focused introduction to AI for business analysts. Participants will learn AI fundamentals, understand how AI technologies interconnect, and gain hands-on experience with leading AI tools such as ChatGPT, Gemini, Copilot, Perplexity, Grok, Claude, Manus, DeepSeek, Qwen, and NotebookLM. The emphasis is on real business analysis use cases, responsible AI usage, and improving the quality and impact of analytical work, without requiring programming skills

Education is undergoing a major transformation driven by Artificial Intelligence (AI). From lesson planning and content creation to assessment design, feedback, and student support, AI offers powerful opportunities to enhance teaching effectiveness, improve learning experiences, and reduce administrative burden—when used responsibly.

Advances in Generative AI and Large Language Models (LLMs) enable educators to personalise learning materials, generate assessments, provide formative feedback, support diverse learners, and streamline academic workflows. However, these opportunities come with challenges related to academic integrity, ethics, bias, data privacy, and the evolving role of educators.

This course provides a practical, education-focused introduction to AI. Participants will learn AI fundamentals, explore how different AI tools support teaching and learning, and gain hands-on experience applying AI to real educational tasks. The emphasis is on pedagogical value, ethical use, and immediate classroom applicability, without requiring technical or programming skills.

Human Resources and Administration functions are evolving rapidly as organisations face increasing complexity in workforce management, compliance, documentation, and employee experience. Artificial Intelligence (AI) is emerging as a powerful enabler to help HR and administrative teams work more efficiently, consistently, and strategically—while preserving human judgment, empathy, and confidentiality.

Advances in Generative AI and Large Language Models (LLMs) allow HR professionals to streamline recruitment documentation, improve internal communication, support policy development, enhance employee engagement, and reduce repetitive administrative workload. However, AI adoption in HR requires careful consideration of ethics, bias, data privacy, and organisational governance.

This course provides a practical, HR- and administration-focused introduction to AI. Participants will learn AI fundamentals, explore how different AI tools support HR and admin workflows, and gain hands-on experience applying AI to real workplace scenarios. The emphasis is on practical productivity gains, responsible usage, and immediate applicability, without requiring technical or programming skills.