Advanced analytical skills for real-world problems: distributed processing (PySpark), predictive workflows, model evaluation and deployment, and communicating insights for business intelligence. Learners manage end-to-end data science workflows, design/evaluate models, and critically assess limitations and ethics.

Self-Assessment Quizzes; Practical Lab Exercises; Assignments; Essays/Reports; Knowledge Checks; Final Exam (Online); Case Studies; Projects

Lectures; Videos; Hands on Labs; Practical Labs; Case Studies; Live Code Demos; Code Reviews; Literature Reading

Apply distributed processing with PySpark; synthesise predictive workflows via the Data Analytics Lifecycle; evaluate models with metrics (RMSE, precision, recall); design/refine supervised models; perform tuning/validation with cross-validation and grid search; communicate insights via effective visualisation/reporting; assess ethical considerations and limitations.

Data transformation and cleaning; Neural networks; Model selection and tuning; Regression and classification modelling; Clustering techniques; Model evaluation metrics and validation strategies

Analytical reasoning; Research and synthesis; Communication of complex results; Evaluation of model reliability/limitations; Ethical judgement in data science practice