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
- CodeSeniorNew
Stand Up a Data Platform for a Mobility-Data Startup's First ML Model
As a 4-person team, build (1) a streaming ingestion path from a simulated telemetry source (Kafka + Python producer is fine); (2) a batch ETL job into a small warehouse (DuckDB …
- Data Engineering
- Streaming Ingestion
- Feature Store
AI Software Engineering Group Project - ResearchSeniorNew
SAT-Based Planner for Smart-Grid Demand Response
Encode the dispatch problem (which customers to curtail by how much, respecting per-customer contractual caps and grid-cell totals) as a SAT or MaxSAT instance. Solve 50 histori…
- Sat Based Planning
- Constraint Encoding
- Benchmarking
Automated Planning - CodeSeniorNew
Train a 3D Object Detector for Highway Trucking
Use the nuScenes or Waymo Open Dataset (open access) as your training and evaluation source. Fine-tune a strong baseline (e.g., CenterPoint or BEVFusion) and define an evaluatio…
- 3d Object Detection
- Perception
- Pytorch
AI for Autonomous Vehicles - ResearchSeniorNew
Trajectory Prediction Model for Urban Robotaxis
Use the Argoverse 2 motion-forecasting dataset (open access). Train an LSTM baseline + a transformer challenger (e.g., a small Wayformer or HiVT). Evaluate on minADE/minFDE (min…
- Trajectory Prediction
- Transformer Models
- Evaluation
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
- DesignSeniorNew
Multi-Region Failover for an Enterprise RAG Service
Design and prototype: (1) a primary-region deployment of the RAG service (vector DB + LLM inference + retrieval API), (2) a passive secondary region with replicated vector store…
- Multi Region Architecture
- Disaster Recovery
- Infrastructure As Code
Cloud Computing for Data and ML - CodeSeniorNew
Grounded Language for a Robotics Pick-and-Place Demo
Use a tabletop simulator (PyBullet or Isaac Sim, both open) with 5 object types and 5 spatial relations (left of, right of, behind, in front of, on top of). Curate or generate a…
- Grounded Language Understanding
- Semantic Parsing
- Perception
Open coursework - CodeSeniorNew
Video Action Recognition for a Retail Loss-Prevention Startup
Use a public action-recognition dataset (UCF101 + a small curated retail-action subset; the latter is provided synthetic or you can label 50 short clips). Fine-tune a small back…
- Video Understanding
- Action Recognition
- Transfer Learning
Computer Vision - ResearchSeniorNew
Solve a POMDP for a Healthtech Diagnostic Pathway
You receive a simplified pathway: 5 possible underlying conditions, 8 possible diagnostic tests each with documented sensitivity and specificity, and an outcome payoff matrix fr…
- Pomdp Modeling
- Belief States
- Approximate Solvers
Decision Making Under Uncertainty - Browse challenges
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Marketing Analyst
Plan and measure campaigns that grow the business. Funnel analytics, attribution, segmentation, and the rigorous measurement that lets marketing defend its budget at the leadership table.
- ResearchSeniorNew
Implement an Autoregressive Model for Anonymized Voice-Synthesis at a Defense Vendor
You receive a public-domain speech dataset (LibriTTS subset, around 50 speakers) and a fixed evaluation protocol (speaker-identifiability AUC, emotion-preservation MOS proxy, in…
- Autoregressive Models
- Voice Conversion
- Speech Synthesis
Deep Generative Models - ResearchSeniorNew
Concept-Activation Vectors for an Autonomous-Vehicle Perception Audit
You receive a trained semantic-segmentation model (8 classes including pedestrian, vehicle, road, sky), an internal validation set of 2,500 driving frames, and a small concept-i…
- Tcav
- Concept Explanations
- Interpretability
Open coursework - ResearchSeniorNew
DPO Preference-Tune a Code Assistant for Style Compliance
You receive a 7B coding base model, a client's published code-style guide (Python, around 80 pages), and a generated preference dataset (4,000 pairs of code snippets where one m…
- Dpo
- Preference Optimization
- Fine Tuning
Fine-Tuning Large Language Models - ResearchSeniorNew
Inductive Logic Programming for a Fraud-Rule Discovery Pilot
You receive a labeled fraud dataset (around 25,000 transactions, around 4% positive class), a feature schema (28 features including device, geo, behavioral history), and a basel…
- Inductive Logic Programming
- Symbolic Ai
- Rule Learning
Open coursework 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
- CodeSeniorNew
Train a GAN for Synthetic Defect Augmentation on a Factory Line
You receive a labeled defect dataset (12 defect types, ranging from 8 to 4,200 examples each), the production classifier, and a starter StyleGAN2-ADA codebase. Train a GAN per r…
- Gans
- Stylegan
- Data Augmentation
Open coursework - DesignSeniorNew
Design Eval Suite for a Multimodal Brainstorming Assistant
You receive (1) the assistant's current API, (2) a list of 6 launch user-personas, and (3) the product team's quality target ('beat the previous model on 4 of 6 personas'). Desi…
- LLM Evaluation
- Multimodal Evaluation
- Safety Evaluation
Generative AI - ResearchSeniorNew
Graph Transformer Research Probe for a Drug-Target Predictor
You receive a public drug-target interaction dataset (around 50,000 drug-target pairs with labels and molecular graphs), a strong GIN baseline, and a starter GraphGPS implementa…
- Graph Transformers
- Graph Neural Networks
- Message Passing
Open coursework - PresentationSeniorNew
Run a Post-Mortem on a Failed ML Deployment
You receive a packet: original training data sample, post-launch production logs, three Slack-style threads from the on-call rotation, and a summary of the telco's complaints. R…
- Root Cause Analysis
- Stakeholder Framing
- Model Monitoring
Open coursework - CodeSeniorNew
Fuse Camera + Audio Cues for an Autonomous-Vehicle Edge Case
You receive a curated dataset of 4,000 short clips (5s each), each with synchronized 8-camera 360-degree video, 4-channel audio, and labels (siren-active emergency vehicle prese…
- Multimodal Perception
- Convolutional Neural Networks
- Audio Processing
Machine Perception - ResearchSeniorNew
Train a Small Diffusion Model for Synthetic Defect Generation
You receive 2,000 labeled defect images and 18,000 clean weld images. Train a small class-conditional latent diffusion model on the defect images (Hugging Face diffusers is fine…
- Generative Perception
- Diffusion Models
- Data Augmentation
Open coursework - ResearchSeniorNew
Plan a Parameter-Efficient Fine-Tuning Strategy for a Big-Tech AI Lab
You will produce (1) a 6-page survey of four PEFT methods (LoRA, adapters, prefix tuning, IA3) with their strengths, weaknesses, and parameter footprints, (2) a one-page decisio…
- Parameter Efficient Fine Tuning
- Transfer Learning
- Fine Tuning
Open coursework - CodeSeniorNew
Survival-Analysis Risk Model for an Oncology Decision-Support Pilot
You receive a curated public colorectal cancer cohort (about 9,000 patients, demographics, stage, grade, comorbidities, baseline labs, censored survival times). Fit (1) a Cox pr…
- Survival Analysis
- Risk Stratification
- Model Calibration
Machine Learning for Healthcare and Biomedicine - CodeSeniorNew
Triage Brain-CT Stroke Detector with Calibrated Uncertainty
You receive a curated public head-CT dataset (about 2,800 scans, slice-level labels for hemorrhagic stroke) and a held-out 600-scan hospital cohort. Train a 3D CNN or 2.5D slice…
- Medical Imaging
- Convolutional Neural Networks
- Uncertainty Quantification
Open coursework - AnalysisSeniorNew
Brain-Tumor MRI Segmentation Bake-Off
You receive a curated public multi-modal MRI brain-tumor cohort (~600 patients, T1/T1c/T2/FLAIR with whole-tumor / tumor-core / enhancing-tumor masks). Train all three architect…
- Medical Imaging
- Segmentation
- Convolutional Neural Networks
Open coursework - ResearchSeniorNew
Self-Supervised Pretraining for a Pathology Foundation Vendor
You receive a public pathology dataset (about 80,000 unlabeled whole-slide-image patches plus a labeled 8,000-patch subtype-classification subset across 4 classes). Pretrain a R…
- Self Supervised Learning
- Medical Imaging
- Transfer Learning
Machine Learning for Imaging and Medical Image Analysis - DesignSeniorNew
Design a Distributed Training Job for a 13B-Parameter Model
Decide whether to use Fully Sharded Data Parallel (FSDP), Tensor Parallelism, Pipeline Parallelism, or a hybrid; justify against the 13B-param + 32-H100 setup. Calculate memory …
- Distributed Training
- Fsdp
- Pytorch
Open coursework
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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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