AI Vibe Coding for Data Mining and Modeling
About This Course
This course equips participants with practical skills to use AI vibe coding and Python for data mining, computational modelling, and insight generation. Learners will use natural-language instructions and AI coding assistants to generate, explain, test, debug, and refine code, enabling them to develop data solutions more efficiently without writing every component manually.
Participants will learn how to collect, clean, transform, and explore structured and unstructured data. They will apply data mining methods such as classification, regression, clustering, association analysis, anomaly detection, similarity analysis, and pattern discovery to address real-world business and technical problems. The course also covers feature engineering, data visualisation, model selection, and the interpretation of complex relationships within datasets.
Building on these foundations, learners will design and evaluate computational models using suitable algorithms and performance metrics. They will use AI-assisted coding to compare modelling approaches, optimise parameters, identify issues such as overfitting and data leakage, and improve model reliability. Where appropriate, learners will also explore graph-based modelling to analyse relationships, communities, connections, and paths within interconnected data.
Through hands-on projects, participants will develop end-to-end Python workflows that transform raw data into validated models, visual findings, and actionable recommendations. By the end of the course, learners will be able to use AI vibe coding to select appropriate algorithms, build reliable data mining models, evaluate results, and communicate insights for evidence-based decision-making.
What You'll Learn
LO1: Develop data science guidelines to enhance data-mining applications.
LO2: Identify and rectify data problems
LO3: Construct graph machine learning models to identify patterns and trends in data sets.
LO4: Perform narrative analytics using Large Language Model (LLM) models
Topics Covered"
Topic 1: Data Science Guidelines and Data Preparation
Topic 2: Graph Data Mining, Pattern Discovery and Algorithm Selection
Topic 3: Graph Machine Learning for Classification, Clustering and Prediction
Topic 4: LLM-Powered Narrative Analytics and Insight Generation
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
Target age group: 21-65 years old