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
- DesignIntermediateNew
Instrument a Model Monitoring Stack from Scratch
Pick the priority product (recommend the customer-service RAG assistant, around 40k queries/day). Define monitoring signals: input drift (Evidently/NannyML), output quality (LLM…
- Model Monitoring
- Data Drift Detection
- LLM Evaluation
ML Engineering and Production ML - CodeIntermediateNew
Automate Retraining with a Drift-Triggered MLflow Pipeline
Stand up the pipeline end to end with the team's existing stack (MLflow tracking + model registry, Airflow orchestration). Wire Evidently to compute weekly drift; when drift cro…
- Mlflow
- Airflow
- Data Drift Detection
ML Engineering and Production ML - DesignIntermediateNew
Design a Negotiation Protocol for Trading Agents
Choose a negotiation framework (alternating-offers Rubinstein, monotonic concession, or auction-based) and justify against the freight use case. Implement a simulator in Python …
- Agent Negotiation
- Game Theory
- Multi Agent Systems
Multi-Agent Systems - CodeIntermediateNew
Build a Vision-Language Search for an E-commerce Catalog
Pick a vision-language encoder (OpenCLIP, SigLIP, or BLIP-2 image-text variant). Index all 600k product images into a vector database (Qdrant/FAISS). Build a query-time pipeline…
- Vision Language Models
- Clip
- Vector Search
Multimodal Machine Learning Develop in-demand professional skills.
Each challenge names the skills it strengthens. Over time, your profile fills with the competences a hiring manager would actually look for.
Why Ewance
- CodeIntermediateNew
Train a Multimodal Classifier for Medical Triage
Pick a fusion architecture (early fusion via cross-attention, late fusion via score combination, or a unified multimodal encoder like FLAVA/CoCa). Train on the 14,000 pairs with…
- Multimodal Fusion
- Cross Attention
- Pytorch
Multimodal Machine Learning - ResearchIntermediateNew
Fine-Tune a Vision-Language Model for Image Captioning
Take BLIP-2 or LLaVA-1.6 as the base. Fine-tune (LoRA is fine) on a 4,000-image accessibility-curated dataset where each image has a useful caption written by a low-vision-exper…
- Vision Language Models
- Lora Fine Tuning
- Pytorch
Multimodal Machine Learning - CodeIntermediateNew
Build a Domain-Specific Named-Entity Recognizer for Legal Contracts
Start from a strong English NER base (spaCy transformer or LegalBERT). Fine-tune on a provided 1,200-contract labeled dataset for the 9 entity types. Handle long contracts (ofte…
- Named Entity Recognition
- Sequence Labeling
- Domain Adaptation
Natural Language Processing - CodeIntermediateNew
Build a Small Transformer from Scratch and Train It on Code
Implement multi-head self-attention, RMSNorm, rotary positional embeddings, and a causal LM head from scratch — no Hugging Face shortcuts for the model code (you may use Hugging…
- Transformers
- Self Attention
- Pytorch
Neural Networks for NLP - Browse challenges
Explore role
Strategy Analyst
Frame the business question, model the options, build the recommendation. From market sizing to competitive analysis, this role is where strategy consulting meets in-house decision-making.
- CodeIntermediateNew
Fine-Tune a Sequence-to-Sequence Model for Code-Doc Generation
Take a small base model (CodeT5+ or a distilled CodeLlama-Instruct). Build the dataset by mining around 8,000 high-quality function-docstring pairs from permissively-licensed Py…
- Seq2seq
- Transformers
- Lora Fine Tuning
Neural Networks for NLP - CodeIntermediateNew
Build a Federated Learning Prototype Across Two Hospitals
Simulate two sites with non-IID data splits (one site skews older, the other younger). Implement FedAvg using Flower (or PySyft). Run for at least 50 communication rounds; repor…
- Federated Learning
- Fedavg
- Secure Aggregation
Privacy-Preserving Machine Learning - StrategyIntermediateNew
Design a PETs Strategy for an EU AI Act Use Case
Map the underwriting use case to applicable PETs across the data-lifecycle stages (training, evaluation, inference, monitoring). For each, document: privacy property gained, acc…
- Pets Strategy
- Differential Privacy
- Federated Learning
Open coursework - CodeIntermediateNew
Generate Synthetic Tabular Data with Privacy Guarantees
Implement DP synthetic data generation: either DP-CTGAN, PATE-GAN, or a marginal-based DP method like PrivBayes / MWEM. Train on the real dataset (around 200,000 transactions, 1…
- Synthetic Data
- Differential Privacy
- Generative Models
Privacy-Preserving Machine Learning Get recognized by recruiters and employers.
Credentials are blockchain-anchored via LearnCoin — tamper-evident, portable, link-shareable on LinkedIn and beyond.
Why Ewance
- CodeIntermediateNew
Diagnose Equipment Failures with a Bayesian Network
You receive 90 days of sensor logs (vibration, spindle temperature, coolant flow, ambient humidity), the maintenance log of 180 failure events labeled by root cause, and a short…
- Bayesian Networks
- Probabilistic Inference
- Parameter Learning
Probabilistic Graphical Models - AnalysisIntermediateNew
Model Patient Pathways with a Hidden Markov Model
You receive de-identified monthly summaries for 8,000 diabetic patients, each row coding the count of primary-care visits, specialist visits, ER visits, new medications, and HbA…
- Hidden Markov Models
- Em Algorithm
- Time Series Modeling
Probabilistic Graphical Models - CodeIntermediateNew
Gaussian Process Regression for Wind Farm Power Curves
You receive 12 months of 10-minute SCADA data (wind speed, air temperature, power output) for 30 representative turbines, plus the manufacturer's published curve. Fit a GP with …
- Gaussian Processes
- Kernel Methods
- Uncertainty Quantification
Probabilistic Machine Learning - AnalysisIntermediateNew
MCMC for Conversion-Funnel A/B Testing at a Marketplace
You receive 6 weeks of per-visitor funnel data (visit, sign-up, trial start, trial-to-paid conversion) split by variant and by acquisition channel (organic, paid social, paid se…
- Mcmc
- Bayesian Hierarchical Models
- Ab Testing
Probabilistic Machine Learning - CodeIntermediateNew
Variational Autoencoder for Synthetic Tabular Banking Data
You receive a 500K-row anonymized transaction dataset with 25 columns (mixed numerical + categorical). Train a VAE (TabVAE or a small custom model) with appropriate likelihoods …
- Variational Inference
- Deep Generative Models
- Synthetic Data
Probabilistic Machine Learning - CodeIntermediateNew
Multi-Turn Dialogue Manager for a Banking Assistant
You receive a transcript dataset of 200 conversations (human-tagged with intent, slot values, and required outcome), a list of 8 supported intents, and tool stubs for 3 backend …
- Dialogue Management
- Intent Classification
- Slot Filling
Question Answering and Conversational Systems - CodeIntermediateNew
Extractive QA on Clinical Trial Protocols
You receive 500 anonymized protocol PDFs (already OCR-ed to text) and 1,200 labeled question-answer pairs where each answer is an exact text span. Build an extractive QA system:…
- Extractive Qa
- Reading Comprehension
- Model Finetuning
Question Answering and Conversational Systems - ResearchIntermediateNew
Policy-Gradient Trading Agent on Historical Data
You receive 5 years of daily OHLCV (Open/High/Low/Close/Volume) data for 5 large-cap stocks. Build an episodic environment where each episode is one calendar year and the agent'…
- Policy Gradients
- Reinforce
- Rl Evaluation
Reinforcement Learning - CodeIntermediateNew
Actor-Critic for Energy-Storage Dispatch
You receive 3 years of hourly day-ahead price data and a Python simulator that models state of charge, round-trip efficiency, and a 1-day price forecast with documented uncertai…
- Actor Critic
- A2c
- Deep Rl
Reinforcement Learning - AnalysisIntermediateNew
Exploration Strategies for a Recommendation Bandit
You receive 60 days of anonymized impression/click logs covering around 200 content items and user features (cohort, listening history bucket). Build a contextual-bandit simulat…
- Contextual Bandits
- Thompson Sampling
- Ucb
Reinforcement Learning - CodeIntermediateNew
RAG Faithfulness Evaluation for a Medical-Education Assistant
You receive 200 student-style questions, two RAG configurations (config A: vector-only + GPT-class generator; config B: hybrid + rerank + GPT-class generator), and the medical-t…
- RAG Evaluation
- Faithfulness
- LLM As Judge
Retrieval-Augmented Generation - CodeIntermediateNew
Agentic RAG with Context-Window Budgeting
You receive a synthetic dataset of 60 founder-style queries paired with 'workspaces' (each up to 500 documents across 3 source types), plus gold-standard answers and citation li…
- Agentic RAG
- Context Window Management
- Iterative Retrieval
Retrieval-Augmented Generation
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Browse challenges aligned to your studies.
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Accept the one that fits your goals.
Step 03
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Step 04
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