Professional Certificate Programme in Data Science with Generative AI

Turn analysis into confident business decisions

Duration: (Weeks 6–9)

Topics Covered

• Probability and Statistics - The Building Blocks of Data Science
• Linear Algebra and Calculus - The Building Blocks of Machine Learning
• Python Programming for Data Science and Machine Learning
• Data Analysis and Visualization for Insight Generation

Sample projects:

• A/B test plan & results
• Cohort retention study
• Exec insight report (2 pages)

Create executive-ready dashboards and stories

Duration: (Weeks 10–13)

Topics Covered:

• Visual theory (chart choice, colour/accessibility, Gestalt, mobile)
• BI modelling
• Calculated fields
• Parameters/filters
• Drill-through & RLS
• Performance optimisation
• Python viz & automation (Plotly, scheduled email)
• Style guide
• Pyramid Principle

Sample projects:

• C-suite revenue dashboard
• Operations KPI board
• Automated weekly distribution

Predictive analytics that drive action

Duration: (Weeks 14–17)

Topics Covered:

• When ML vs rules
• Feature engineering
• Supervised/unsupervised
• Business evaluation (cost of error, ROI)
• Model cards
• AutoML(PyCaret/Vertex/Azure)
• Deployment endpoints
• MLflow tracking
• Monitoring dashboards & A/B Testing of models

Sample projects:

• Churn prediction & playbook
• Demand forecast with CIs
• Price elasticity & optimisation

GenAI as your analytics multiplier

Duration: (Weeks 18–20)

Topics Covered:

• SQL/code generation
• Automated EDA
• Anomaly explanations
• NL Q&A for dashboards
• RAG over company knowledge
• Workflow automation(orchestration)
• Governance: validation, bias, privacy, cost control

Sample projects:

• AI-augmented analysis pipeline
• RAG Q&A over metrics
• Time-reduction evidence note

Duration: (Weeks 21–24)

Phases:

• W21 charter/data
• W22 analysis & model
• W23 BI build + GenAI + user test
• W24 exec presentation, adoption & monitoring plan

Track options:

• Customer Intelligence
• Revenue Optimisation
• Operational Excellence