Cluster a Telco's Subscriber Base for a Pricing Refresh
Overview
What this challenge is about.
Cluster a Telco's Subscriber Base for a Pricing Refresh. Intermediate challenge in analysis. Analyzing real datasets and building models that drive decisions...
The Brief
What you'll do, and what you'll demonstrate.
Identify 3-6 behaviorally distinct subscriber segments and propose a refreshed prepaid tier structure that reduces cannibalization.
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 preprocessing and feature scaling appropriate to behavioral data
- Compare multiple clustering algorithms and select with stability evidence
- Translate cluster centroids into named, interpretable personas
- Connect a segmentation to a concrete business action (pricing)
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Data Mining and Knowledge Discovery
Master · Data Engineering
Strong alignment
This challenge maps to Data Mining and Knowledge Discovery 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.
- Clustering
Apply clustering to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering 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.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Scikit Learn
Apply scikit learn to solve real industry problems and demonstrate production-level capability.
- Segmentation
Apply segmentation 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:
Data Scientist
Behavioral segmentation tied to a pricing decision is a canonical junior-data-scientist first project in any consumer-facing business.
This challenge sharpens
- clustering
- segmentation
- exploratory-data-analysis
Applied AI Scientist
Stability analysis plus business framing mirrors applied AI work in product or consumer teams.
This challenge sharpens
- clustering
- scikit-learn
- segmentation
AI Product Manager
Translating cluster outputs into pricing tiers and revenue impact maps directly onto AI product manager interview cases.
This challenge sharpens
- segmentation
- exploratory-data-analysis
- feature-engineering