Industry-grade training for early-career data professionals
I design and deliver intensive, cohort-based training tracks for STEM graduates and early-career professionals who want to work in analytics, data science and machine learning.
The focus is not on tools in isolation, but on how real teams make decisions:
working with imperfect data, designing experiments, building models that survive production constraints, and communicating results to non-technical stakeholders.
All tracks run over 12 weeks, are live, and are designed for people who are willing to treat this as serious professional training—not casual coursework.
Causal Inference & Experimentation
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.
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.
Marketing & Monetisation Analytics
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.
Machine Learning & MLOps
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.
Engagement Model
Training is delivered in small cohorts to allow for interaction, feedback and realistic project work.
Depending on availability and goals, services can be delivered as:
- Cohort-based training tracks
- Premium mentored tracks with additional 1:1 support
- Bespoke training for individuals or teams
Pricing depends on track, cohort size and level of mentoring.