Industry-grade training for early-career data professionals

This track focuses on how modern data teams answer “what caused what?” in product, marketing and policy settings.

 

Participants learn how to design experiments, estimate causal effects, and reason under uncertainty using methods that are widely used in industry.​

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Key areas

• A/B testing and experimental design

• Causal inference with observational data

• Difference-in-differences, synthetic control, causal graphs

• Communicating causal results and assumptions

 

Best suited for candidates aiming for data scientist, product analyst or experimentation roles.

This track is designed for people who want to work at the intersection of data, marketing and revenue decisions.

 

The emphasis is on incrementality, experimentation and optimisation—moving beyond naive attribution and toward methods that actually inform budget and pricing decisions.

 

Key areas

• Marketing experiment design and incrementality

• Marketing Mix Models (MMM) and their limitations

• Budget allocation and ROI analysis

• Pricing experimentation and monetisation strategy

 

Best suited for marketing analysts, growth analysts and data scientists working with commercial teams.

This track focuses on building machine learning systems that work beyond notebooks.

 

Participants learn how to frame ML problems correctly, evaluate models in a business context, and understand the practical constraints of deployment, monitoring and iteration.

 

Key areas

• Problem framing, feature engineering and leakage prevention

• Model evaluation and business-aligned metrics

• Deployment concepts (batch vs real-time)

• Monitoring, drift and experimentation with ML systems

 

Best suited for candidates targeting applied ML, data science or ML engineering roles.