Forecast Daily Demand for an Apparel Supply-Chain Team
Overview
What this challenge is about.
Forecast Daily Demand for an Apparel Supply-Chain Team. Intermediate challenge in analysis. Analyzing real datasets and building models that drive decisions,...
The Brief
What you'll do, and what you'll demonstrate.
Pick the demand-forecasting approach that gives the best MASE improvement over the SARIMA baseline at acceptable training cost.
This is not a data exercise. It is the work an analyst does when stakeholders need answers from messy data. That distinction matters to every hiring manager who has seen candidates describe statistical methods and none who have extracted insight from messy, real-world data.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Apply SARIMA, tree-based, and neural forecasters to real retail data
- Evaluate forecasts with rolling-origin folds (not random split)
- Quantify bias and error by forecast horizon
- Communicate a forecasting recommendation to a supply-chain audience
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.
- Time Series Forecasting
Apply time series forecasting to solve real industry problems and demonstrate production-level capability.
- Sarima
Apply sarima to solve real industry problems and demonstrate production-level capability.
- Gradient Boosting
Apply gradient boosting to solve real industry problems and demonstrate production-level capability.
- Neural Forecasting
Apply neural forecasting to solve real industry problems and demonstrate production-level capability.
- Rolling Evaluation
Apply rolling evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Forecasting at SKU-store scale with rigorous rolling evaluation is the textbook senior data-scientist project at any large retailer.
This challenge sharpens
- time-series-forecasting
- rolling-evaluation
- sarima
Machine Learning Engineer
Comparing classical, tree-based, and neural forecasters across cost and accuracy is the same trade-off MLEs make for production deployment.
This challenge sharpens
- gradient-boosting
- neural-forecasting
- rolling-evaluation
Applied AI Scientist
Translating forecast error analysis into a supply-chain memo is the bread and butter of applied-AI work in operations-heavy retail.
This challenge sharpens
- time-series-forecasting
- neural-forecasting
- sarima