Recommendation Systems Analytics

Course Overview

A practical, data‑science‑focused course that teaches learners how to design, build, and evaluate recommendation systems used in e‑commerce, streaming, retail, and digital platforms. The course covers collaborative filtering, content‑based models, hybrid approaches, user–item similarity, ranking algorithms, A/B testing, personalisation strategies, and model performance evaluation.

Target Audience

• Data analysts, data scientists, and machine‑learning practitioners

• E‑commerce, product, and digital‑experience teams

• Marketing, CRM, and personalisation specialists

• BI developers and decision‑support teams

• Students or professionals entering data science, AI, or digital product roles

Course Outcomes

• Understand recommendation system types, architectures, and business applications

• Build collaborative filtering, content‑based, and hybrid recommendation models

• Analyse user behaviour, item attributes, and interaction patterns

• Apply similarity metrics, ranking algorithms, and matrix factorisation techniques

• Evaluate model performance using precision, recall, MAP, NDCG, and A/B testing

• Use recommendations to improve engagement, retention, and conversion

• Build dashboards to monitor recommendation performance and user impact

• Communicate insights and model results clearly to product, marketing, and leadership teams