Optimizing Inventory for a Toronto D2C Cosmetics Brand

FreeVerified credential2 weeksIntermediate

Overview

What this challenge is about.

Optimizing Inventory for a Toronto D2C Cosmetics Brand. Intermediate challenge in design. Designing real products under real constraints, earn a blockchain-v...

CredentialBlockchain-anchored

ShareableLinkedIn-ready

LanguageEnglish

PaceSelf-paced

The Brief

What you'll do, and what you'll demonstrate.

Design and implement a BI system to reduce stock-outs by 20% and decrease excess inventory by 15% within three months.

This is not a design exercise. It is the work a product designer does between a brief and a shipped interface. That distinction matters to every hiring manager who has seen candidates redesign Spotify's homepage and none who have worked under real product constraints.

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 dimensional modeling (star schema) to a business scenario
Design and implement an ETL process for data integration
Use OLAP operations (drill-down, slice, dice) to analyze inventory data
Create a BI dashboard that supports decision-making
Evaluate inventory performance using key metrics

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.

Careers

Career paths this challenge builds toward

Completing this challenge demonstrates skills that transfer directly to these roles:

Career paths this builds toward

Canonical roles

Data Engineer
Every clever dashboard, every machine-learning model, every executive report rests on plumbing that someone built and maintains. Data engineers build that plumbing. They design the pipelines that move information from production systems into warehouses where analysts and scientists can use it, and they keep those pipelines reliable as data volumes grow. The role suits students who enjoy systems thinking and care about how things actually work under load. Days involve writing transformations in dbt, scheduling jobs in Airflow, and quietly catching bad data before it reaches a decision maker. Excellence shows as pipelines nobody notices because they never break. You grow into this by writing a lot of SQL, picking up Python, and learning one cloud platform deeply.
Data Science

One more thing

You can put a credential on your CV by Friday.

Start this challenge