Data Sciences Challenges
Explore data science challenges on Ewance to build skills employers expect from analysts and ML engineers. Work through challenges on data cleaning, exploratory analysis, modeling, and storytelling with data.
Most Popular
- PresentationBeginnerNew
Storytelling Visualization of an Autonomous Vehicle Test Campaign
You receive aggregated test results: 12,000 test runs across dry, wet, and snow conditions, with metrics for disengagement rate, near-miss count, and route-completion percentage…
- Data Storytelling
- Audience Adaptation
- Chart Design
Data Visualization - CodeBeginnerNew
Build a Real-Time Operations Wall Display for a Logistics AI Startup
You receive a websocket feed of operational events (around 20 events per second) plus a small KPI definition list (throughput per zone, late-truck count, exception queue depth, …
- Realtime Visualization
- Glanceability
- D3
Data Visualization - DesignBeginnerNew
Build a Multi-Criteria Vendor Selection Tool for an AI Consulting Firm
You will design a Multi-Criteria Decision Analysis (MCDA, a structured way to score options against weighted criteria) tool that accepts 6-10 vendor options, 6-12 weighted crite…
- Mcda
- Decision Support Systems
- Streamlit
Decision Support Systems and Decision Analysis - DesignBeginnerNew
Design a Negotiation Support Tool for Climate-Tech Supplier Contracts
You will design and prototype a negotiation support tool for a single supplier contract with six issues (price per kg, delivery lead time, minimum order quantity, payment terms,…
- Negotiation Modeling
- Decision Support Systems
- Multi Issue Bargaining
Decision Support Systems and Decision Analysis 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
- CodeBeginnerNew
Build a Crop-Disease Classifier for a Smallholder Agritech Startup
You receive a curated 22,000-image cassava-disease dataset across 5 classes (4 diseases + healthy) plus a labeled 1,200-image held-out test set. Train a CNN classifier (start wi…
- Cnn Classification
- Cnn Architectures
- Transfer Learning
Deep Learning for Computer Vision - CodeBeginnerNew
Build an MLP Baseline for Credit-Default Risk at a Fintech
You receive 18 months of anonymized credit-decision data (around 600,000 applications, 80 features) with a 90-day default label. Train an MLP with regularization (dropout, weigh…
- Mlp
- Regularization
- Tabular Deep Learning
Deep Learning - CodeBeginnerNew
Quantize a Vision Model for a Smart-Doorbell SoC
You receive a trained FP32 PyTorch person-detector (mAP 0.74 on a 5k validation set) plus a calibration dataset of 500 unlabeled doorbell frames. Convert to ONNX, then apply pos…
- Quantization
- Model Optimization
- Onnx Optimization
Edge ML and On-Device Machine Learning - CodeBeginnerNew
Optimize Wind-Turbine Layout with a Genetic Algorithm
You receive a wind-speed-and-direction time series for the lease area, the polygon boundary, a minimum inter-turbine spacing constraint, and a Jensen wake model. Implement a rea…
- Genetic Algorithms
- Metaheuristics
- Constraint Handling
Evolutionary Computation and Metaheuristic Search - Browse challenges
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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.
- ResearchBeginnerNew
Hyperparameter Search via CMA-ES for a Pharma QSAR Model
You receive a labeled QSAR dataset (around 25,000 compounds, regression on a binding-affinity target), a fixed feature pipeline (Morgan fingerprints + descriptors), and the team…
- Cma Es
- Metaheuristics
- Hyperparameter Optimization
Evolutionary Computation and Metaheuristic Search - CodeBeginnerNew
Simulated Annealing for Shift Scheduling at a Hospital
You receive 6 months of anonymized shift demand data, the nurse roster (skills, certifications, contracted hours), and the labor-law hard constraints. Encode the schedule as a 7…
- Simulated Annealing
- Metaheuristics
- Constraint Handling
Evolutionary Computation and Metaheuristic Search - AnalysisBeginnerNew
Explain a Credit-Risk Model with SHAP for a Fintech
You receive a trained XGBoost credit-risk model (binary default prediction), the training feature schema (38 features), and a held-out 10,000-sample test set with labels. Comput…
- Shap
- Interpretability
- Fairness Analysis
Explainable and Interpretable AI - AnalysisBeginnerNew
Interpretable-by-Design GAM for an Insurer's Claims Triage
You receive an anonymized claims dataset (around 60,000 claims, target: log reserve), a feature schema (22 features), and an existing LightGBM baseline (held-out R^2 of 0.78). T…
- Generalized Additive Models
- Ebm
- Interpretability
Explainable and Interpretable AI 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
- CodeBeginnerNew
Fuzzy-Logic Controller for a Sustainable-Greenhouse Operator
You receive a year of 15-minute climate logs (inside/outside temperature, humidity, light, CO2), the current rule-based controller, and the head grower's qualitative description…
- Fuzzy Logic
- Mamdani Inference
- Rule Based Systems
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - AnalysisBeginnerNew
Community Detection on a Pharma Clinical-Trial Investigator Graph
You receive a pre-fetched dump of around 15,000 trials from a public registry covering oncology over the last 10 years and a mapping of trials to investigator names + institutio…
- Community Detection
- Louvain
- Leiden
Machine Learning on Graphs - AnalysisBeginnerNew
Spectral Clustering for an Urban-Mobility Operator's Network
You receive 6 months of anonymized O-D trip data (around 4 million trips, around 8,000 virtual stations), the current 9 hand-drawn zones, and the operations team's KPIs (rebalan…
- Spectral Methods
- Spectral Clustering
- Graph Laplacian
Machine Learning on Graphs - ResearchBeginnerNew
Evaluate a Generative AI Image Tool with a Within-Subjects Study
You will write a study protocol, recruit 20 participants (a Discord callout is fine), counterbalance the two conditions, and run 45-minute sessions over Zoom. Collect three meas…
- Experimental Design
- User Study
- Within Subjects Design
Human-Computer Interaction for AI Systems - AnalysisBeginnerNew
Diagnose Query Failures in an E-Commerce Search Box
You receive 6 months of anonymized query logs (~480 million rows): query string, language hint, results-shown count, top-3 product clicks, and add-to-cart events. Build a notebo…
- Query Log Analysis
- Clustering
- Ir Failure Analysis
Information Retrieval and Search - DesignBeginnerNew
Design a Retrieval Pipeline for a Climate-Research Open Archive
You receive a metadata sample (5,000 documents) plus 50 example researcher queries (mixed-language). Design a retrieval pipeline architecture that: (1) extracts and normalizes s…
- Retrieval Architecture
- Hybrid Search
- Multilingual Search
Information Retrieval and Search - CodeBeginnerNew
Build a Product Knowledge Graph for a Fast-Fashion Retailer
You receive 200 sample SKUs across 4 markets (Spain, Germany, Japan, Brazil) as CSVs with country-specific attribute names. Design an OWL ontology with shared classes for Produc…
- Knowledge Graphs
- Owl Ontology
- Rdf
Knowledge Graphs and Semantic Web - ResearchBeginnerNew
Curate a Domain Lexicon for a Climate-Tech NLP Stack
You receive 5,000 policy documents and a benchmark of 200 documents with manually tagged domain terms. Curate a lexicon of ~1,500 terms with (1) canonical English form, (2) Swah…
- Lexical Resources
- Named Entity Recognition
- Spacy
Linguistic Engineering and Language Technologies - 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) - 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
Machine Learning (Undergraduate) - AnalysisBeginnerNew
Stress-Test a Hiring-Funnel Model for Bias
You receive a synthetic-but-realistic dataset of 25,000 past applicants with features (years of experience, education tier, prior role tags) and outcome labels (advanced past th…
- Model Evaluation
- Fairness Metrics
- Logistic Regression
Machine Learning (Undergraduate) - StrategyBeginnerNew
Scope a Demand-Forecasting Model with Operations Stakeholders
You receive recorded interview transcripts (or summary notes) for the three personas, plus a sample of the historical sales data. Map each stakeholder's pain to candidate ML pro…
- Stakeholder Framing
- Ml Problem Scoping
- Metric Design
Machine Learning in Practice
How it works
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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