Overview
What this challenge is about.
Stack gradient boosting, Bayesian Ridge, and a neural net with rolling time-series CV to report ROI uplift per brand. Earn your verifiable certificate.
The scenario
The startup (around 50 staff, around USD 6M ARR) currently sells per-brand MMM dashboards and is under competitive pressure from larger US attribution vendors entering the Indian market.
The Brief
What you'll do, and what you'll demonstrate.
Determine whether a stacked ensemble of three model families improves marketing-mix-modelling robustness over a gradient-boosting baseline.
Earning criteria — what you'll demonstrate
- Build and evaluate a stacked ensemble across heterogeneous model families
- Apply rolling-origin cross-validation correctly for time-series problems
- Quantify robustness, not just point accuracy, of marketing-mix predictions
- Translate model results into business-relevant ROI uplift
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Ensemble Methods
Apply ensemble methods to solve real industry problems and demonstrate production-level capability.
- Stacking
Apply stacking to solve real industry problems and demonstrate production-level capability.
- Time Series Cv
Apply time series cv to solve real industry problems and demonstrate production-level capability.
- Regularization
Apply regularization to solve real industry problems and demonstrate production-level capability.
- Bayesian Regression
Apply bayesian regression to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Applied AI Scientist
Designing stacked ensembles that improve a real production metric is the applied-AI-scientist's signature deliverable in marketing or analytics teams.
This challenge sharpens
- ensemble-methods
- stacking
- model-evaluation
ML Researcher
Comparing heterogeneous learners with time-series CV and Bayesian components is the kind of rigour ML-research interviewers probe for.
This challenge sharpens
- bayesian-regression
- time-series-cv
- ensemble-methods
Data Scientist
Stacking pipelines and per-channel robustness reporting are skills senior data-scientist roles require for any attribution or causal-impact team.
This challenge sharpens
- regularization
- stacking
- model-evaluation