Feature engineering
If you like applying Feature engineering, every challenge here gives you a chance to practice it on a real industry brief.
- AnalysisBeginnerNew
Diagnose Churn Drivers for a B2B SaaS Workflow Tool
You receive three CSV exports: 18 months of weekly product-usage events for about 1,800 accounts, the full support-ticket history, and account firmographics (industry, size, pla…
- Exploratory Data Analysis
- Data Wrangling
- Feature Engineering
Applied Data Analysis and Practical Data Science - CodeBeginnerNew
Stack Five Models for a Kaggle-Style Forecasting Bake-Off
You receive a pseudonymized dataset of 24 months of daily shipment volumes across about 200 origin-destination lanes plus weather and holiday features. Train 5 base models, use …
- Ensemble Methods
- Time Series Forecasting
- Feature Engineering
Advanced Machine Learning - AnalysisBeginnerNew
Predict Equipment Failure for a Wind-Farm Operator
You receive 18 months of SCADA (Supervisory Control and Data Acquisition — the standard turbine telemetry feed) data sampled every 10 minutes from all 240 turbines, with labeled…
- Classification
- Regularized Regression
- Gradient Boosting
Open coursework - AnalysisBeginnerNew
Analyze a Learning-Analytics Dataset for At-Risk Detection
You receive an anonymized dataset of LMS engagement features (logins, assignment submissions, forum posts, video-watch time), grade history, and a binary label for end-of-semest…
- Learning Analytics
- Classification
- Fairness Metrics
Open coursework Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- AnalysisBeginnerNew
Spectral-Analyze Wearable Sleep Data for a Healthtech Pilot
You receive 30 nights of wearable data per 25 volunteers, with polysomnography-derived ground-truth stages (Wake / NREM / REM). Engineer spectral features (delta, theta, alpha, …
- Spectral Analysis
- Feature Engineering
- Wavelet Analysis
Open coursework - AnalysisBeginnerNew
Predict 30-Day Churn for a Direct-to-Consumer Cosmetics Brand
Working only from the two materials you are given (a subscriber behavior sample dataset and a one-page stakeholder brief from the head of retention), build a binary classificati…
- Python
- Scikit Learn
- Logistic Regression
Open coursework - AnalysisBeginnerNew
Investigate Churn Drivers for NordicGlow Cosmetics Customers
Work only from the two materials NordicGlow has handed you: the customer behaviour dataset (ds-nordicglow-customers) and the founder's retention brief (brief-nordicglow-ceo). Be…
- Data Preprocessing
- Feature Engineering
- Classification
Open coursework - AnalysisBeginnerNew
Cluster a Telco's Subscriber Base for a Pricing Refresh
You receive 12 months of anonymized subscriber-level data: monthly minutes, SMS, mobile data, top-up frequency, top-up amount, churn flag, and tenure. Clean and feature-engineer…
- Clustering
- Feature Engineering
- Exploratory Data Analysis
Data Mining and Knowledge Discovery - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- CodeBeginnerNew
Churn-Prediction Model for a B2B Vertical SaaS
Use 18 months of anonymized data (provided) covering: usage events, login frequency, support tickets, NPS responses, billing health, feature adoption, practice firmographics. De…
- Supervised Learning
- Python Programming
- Ml Applications
Machine Learning (CS Elective) - AnalysisIntermediateNew
Forecasting Churn for a SaaS Scale-Up
You are a data scientist intern at TaskFlow. Using the provided dataset, perform feature engineering and build a logistic regression or decision tree model to predict churn. Ide…
- Data Analysis
- Regression
- Classification
Open coursework - CodeIntermediateNew
Predict Loan Default Risk for a Cross-Border Fintech
You receive 18 months of transactions (around 12M rows) and seller-firmographic data. Define a defensible proxy label for default (e.g., a 60-day chargeback-or-dispute spike com…
- Feature Engineering
- Model Selection
- Model Evaluation
Open coursework - CodeBeginnerNew
Ship a Lightweight ML Microservice for an EdTech Reading App
You receive 3 months of session telemetry (around 50M reading events, child-anonymized). Engineer features per session window, train a small classifier (logistic regression base…
- Feature Engineering
- Model Serving
- Containerization
Applied Machine Learning Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- CodeIntermediateNew
Forecast Energy Demand for a Nordic Renewable Utility
You receive 5 years of hourly residential-segment demand, hourly weather data (temperature, wind, irradiance), and a calendar of public holidays. Build a probabilistic forecaste…
- Time Series Forecasting
- Probabilistic Modeling
- Feature Engineering
Applied Machine Learning - AnalysisBeginnerNew
Detect Fraudulent Refund Requests for a Mid-Market Marketplace
You receive a labeled dataset with buyer history, seller history, shipping carrier, refund reason text, and outcome label (legit / fraud). Train and evaluate at least two classi…
- Classification
- Model Calibration
- Imbalanced Classification
Open coursework - CodeIntermediateNew
Build an Anomaly-Detection Pipeline for Pharma Cold-Chain Logistics
You receive 18 months of shipment telemetry (around 60,000 shipments, around 12 million sensor readings) plus a hand-labeled set of 1,200 incidents (mix of true excursions, sens…
- Anomaly Detection
- Feature Engineering
- Time Series
Data Mining and Knowledge Discovery - CodeBeginnerNew
Ship a Churn-Prediction Mini-Project End to End
You receive a 12-month anonymized dataset of subscriber events (logins, lesson completions, payment history, support tickets) for around 200,000 users. Define churn precisely (n…
- Feature Engineering
- Model Evaluation
- Gradient Boosting
Open coursework - CodeIntermediateNew
Forecast Intraday FX Volatility for a London Liquidity Desk
You receive 18 months of tick-level mid-quote data for six FX pairs plus a calendar of scheduled macro events. Resample to 1-minute bars, engineer realized-volatility features, …
- Time Series Forecasting
- Feature Engineering
- Model Validation
Open coursework - CodeIntermediateNew
Detect Coordinated Fraud Rings via Link Analysis at a Neobank
You receive 90 days of account, login, and transaction data (around 1.2 million accounts, around 30 million events) plus a labeled set of 80 known fraud rings. Build a multi-rel…
- Graph Analysis
- Community Detection
- Link Analysis
Open coursework - CodeIntermediateNew
Build a Feature Store Backbone for a Healthtech ML Team
You receive synthetic wearable telemetry (heart rate, accelerometer, sleep stages) for around 5,000 patients across 90 days, plus the existing scattered feature scripts from the…
- Feature Engineering
- Data Modeling
- Python
Data Engineering and Big Data Systems - AnalysisIntermediateNew
Capstone Lab: Diagnose Why a Production Model Quietly Stopped Working
You receive 6 months of production logs (model inputs, predictions, ground truth from chargebacks) plus the original training data and model card. Reproduce the recall drop in a…
- Data Drift Detection
- Model Monitoring
- Root Cause Analysis
Open coursework - AnalysisBeginnerNew
Re-segment a SaaS Customer Base by How Accounts Actually Use the Product
Using the provided usage signals dataset, engineer features that capture how deeply and how often accounts use the product (not just raw volume), standardize them, and run at le…
- Clustering
- K Means
- Hdbscan
Open coursework - CodeIntermediateNew
Build a Real-Time Streaming Pipeline for Card-Fraud Scoring
Using the provided transaction-event sample, the cardholder feature snapshot, the reference scoring service, and the risk team's experiment specification, build a stream-process…
- Stream Processing
- Kafka
- Flink
Open coursework - CodeBeginnerNew
Predict Subscription Churn for an EdTech Platform
You receive a CSV with about 18,000 student-month rows: features include login frequency, session length, quiz scores, parent app opens, and plan tier. The target is whether the…
- Supervised Learning
- Logistic Regression
- Gradient Boosting
Machine Learning (Undergraduate) - CodeBeginnerNew
Predict Catalyst Properties for a Green-Hydrogen Pharma Spinout
Use an open catalyst dataset (e.g., Open Catalyst Project subset, or a Materials Project pull) where each candidate has descriptors and a target activity property. Train a tabul…
- Tabular Modeling
- Uncertainty Quantification
- Feature Engineering
AI for Science and Engineering
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From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
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