Build a Reproducible Pricing Analysis for a DTC Skincare Brand
Overview
What this challenge is about.
Build a Reproducible Pricing Analysis for a DTC Skincare Brand. Intermediate challenge in analysis. Analyzing real datasets and building models that drive de...
The Brief
What you'll do, and what you'll demonstrate.
Recommend SKU-level price changes backed by elasticity estimates and cohort-impact projections, delivered as a one-command-rerun pipeline.
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
- Wrangle commerce data with realistic mess (returns, partial refunds, currency)
- Estimate price elasticity from observational data and state caveats
- Build a cohort view and explain what the curve does and doesn't say
- Package the analysis as a re-runnable artifact, not a one-shot notebook
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Applied Data Analysis and Practical Data Science
Master · Data Engineering
Strong alignment
This challenge maps to Applied Data Analysis and Practical Data Science at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Data Wrangling
Apply data wrangling to solve real industry problems and demonstrate production-level capability.
- Exploratory Data Analysis
Apply exploratory data analysis to solve real industry problems and demonstrate production-level capability.
- Cohort Analysis
Apply cohort analysis to solve real industry problems and demonstrate production-level capability.
- Regression Modeling
Apply regression modeling to solve real industry problems and demonstrate production-level capability.
- Reproducible Analysis
Apply reproducible analysis 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
Pricing analysis with elasticity estimation and cohort retention is the bread-and-butter project portfolio of a junior data scientist at a DTC or consumer-tech company.
This challenge sharpens
- regression-modeling
- cohort-analysis
- exploratory-data-analysis
Data Engineer
Turning a notebook into a one-command rerunnable pipeline with documented inputs/outputs is the entry point to data-engineering work.
This challenge sharpens
- reproducible-analysis
- data-wrangling
- python
Applied AI Scientist
Building a defensible quantitative recommendation a Chief Financial Officer can act on mirrors applied AI work: model + business reasoning + clear communication.
This challenge sharpens
- regression-modeling
- cohort-analysis
- reproducible-analysis