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
- CodeIntermediateNew
Instruction-Tune a Small Model for an Edtech Tutor
You receive a 1.5B base model (e.g., SmolLM-1.7B or Qwen-1.8B), permission to use 2 hours of a rented A100, and a curated seed of around 5,000 math-tutoring dialogues. Augment w…
- Instruction Tuning
- Fine Tuning
- Dataset Curation
Fine-Tuning Large Language Models - AnalysisIntermediateNew
Catastrophic-Forgetting Audit on a Domain Fine-Tune
You receive the fine-tuned 7B chemistry model and its base, plus a benchmark basket (MMLU subset, GSM8K, IFEval, a small instruction-following set). Run all 4 benchmarks on both…
- Catastrophic Forgetting
- LLM Evaluation
- Fine Tuning
Fine-Tuning Large Language Models - DesignIntermediateNew
Build an OWL Ontology for a Pharma R&D Knowledge Base
You receive a CSV-form starter knowledge base (around 4,000 compounds, 600 targets, 1,200 assays) and a list of 12 competency questions the scientists currently can't answer wit…
- Ontology Design
- Owl
- Knowledge Representation
Fuzzy Logic, Knowledge Representation, and Symbolic Reasoning - ResearchIntermediateNew
Evaluate VAEs vs. Diffusion for Synthetic Tabular-Data Generation
You receive a real labeled dataset (around 18,000 anonymized patient records, 32 features, binary outcome) and the team's existing VAE baseline. Train a tabular diffusion model …
- Tabular Diffusion
- Vae
- Synthetic Data
Generative AI 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 GNN for Fraud-Ring Detection at a Payments Fintech
You receive an anonymized transaction dataset (around 120,000 merchants, around 4 million transactions over 12 months, around 2% labeled fraud) and the team's LightGBM baseline.…
- Neural Networks
- Graphsage
- Fraud Detection
Machine Learning on Graphs - CodeIntermediateNew
Link Prediction for a B2B SaaS Account-Expansion Engine
You receive a CSV of around 80,000 accounts (existing customers + prospects) with attributes (industry, size, tech stack, geography) plus 18 months of marketing-touch and conver…
- Link Prediction
- Node Embeddings
- Node2vec
Machine Learning on Graphs - CodeIntermediateNew
Design Safe Navigation Behavior for a Hospital Delivery Robot
You receive a dataset of 200 anonymized hospital corridor traces (people positions over time from the robot's LIDAR) plus the current planner's parameters. Design a policy that …
- Human Aware Navigation
- Ros2
- Motion Planning
Human-Robot Interaction - CodeIntermediateNew
Teach a Warehouse Cobot from Operator Demonstrations
You receive a simulated UR5e cobot in PyBullet, plus 12 example demonstrations of two kitting sequences. Implement Dynamic Movement Primitives (DMPs — a classic LfD technique th…
- Learning From Demonstration
- Dynamic Movement Primitives
- Human Robot Interaction
Human-Robot Interaction - Browse challenges
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Pricing Strategist
Set the price that captures value without leaving sales on the table. Demand modelling, willingness-to-pay research, and the disciplined experimentation that turns pricing into a competitive advantage.
- CodeIntermediateNew
Map a Climate-Policy Corpus to Linked Open Data
You receive 12,000 policy PDFs and a benchmark of 200 documents with manually linked entities (places, organizations, policies). Build a pipeline that runs NER, candidate-genera…
- Entity Linking
- Linked Open Data
- Wikidata
Knowledge Graphs and Semantic Web - DesignIntermediateNew
Design a Customer 360 Graph for a Cross-Border Fintech
You receive 500 sample customer records across CRM, payments core, and KYC systems, plus a 50-record entity-resolution benchmark (pairs labelled same/different). Design an OWL o…
- Customer 360
- Entity Resolution
- Owl Ontology
Knowledge Graphs and Semantic Web - ResearchIntermediateNew
Evaluate a Knowledge-Graph-Augmented Recommender
You receive permission to use the public MovieLens 1M dataset plus a derived item-KG (movie -> genre, director, decade) built from Wikidata. Train two recommenders: a matrix-fac…
- Knowledge Graph Embeddings
- Recommender Systems
- Benchmarking
Knowledge Graphs and Semantic Web - CodeIntermediateNew
Build an Evaluation Harness for an Internal LLM Assistant
You will design and implement an evaluation harness in Python that runs four test suites: (1) helpfulness (LLM-as-judge with rubric), (2) factual grounding (compare cited source…
- LLM Evaluation
- LLM As Judge
- Prompt Injection Testing
Large Language Models Get recognized by recruiters and employers.
Credentials are blockchain-anchored via LearnCoin — tamper-evident, portable, link-shareable on LinkedIn and beyond.
Why Ewance
- ResearchIntermediateNew
Run an Alignment Probe on a Coding Assistant
You will design 240 probe prompts across 3 classes: (1) over-refusal (innocuous coding asks the model should fulfill), (2) insecure code patterns (asks where the model should wa…
- Red Team Operations
- Alignment Evaluation
- LLM Evaluation
Large Language Models - CodeIntermediateNew
Build a Domain Instruction-Tuning Recipe for a Legal Coach
You will source instruction data from three streams: ~3,000 synthetic paralegal Q&A generated by a frontier model (anonymized prompts), ~1,500 curated examples from public legal…
- Instruction Tuning
- Fine Tuning
- Data Curation
Large Language Models - CodeIntermediateNew
Mine Health-Forum Posts for Symptom Trend Signals
You receive 6 months of crawled public posts (~400,000 posts, already cleaned of usernames) and access to a UMLS API for normalisation. Build a pipeline that does (1) symptom ex…
- Text Mining
- Biomedical NLP
- Umls Normalization
Linguistic Engineering and Language Technologies - CodeIntermediateNew
Build a Tool-Calling Agent for an Internal Reporting Bot
You will implement the agent in either LangChain or LlamaIndex (your choice; defend it in the readme). Wire 4 tools: (1) read-only SQL on a sample warehouse, (2) a mocked BI met…
- Agent Orchestration
- Tool Calling
- Langchain Or Llamaindex
LLM Application Development - CodeIntermediateNew
Design Prompt Versioning and Observability for a Coding Assistant
You will (1) design a prompt-registry data model (versions, owners, environments, change log) and implement it in Postgres + a small Python SDK, (2) instrument the assistant to …
- Prompt Versioning
- Observability
- Pii Scrubbing
LLM Application Development - CodeIntermediateNew
Build an End-to-End ML Pipeline for Loan-Default Prediction
You receive 24 months of historical application + outcome data (about 380,000 rows). Build a pipeline using a workflow orchestrator (Prefect, Kedro, or a simple Makefile chain) …
- Ml Pipelines
- Feature Engineering
- Pipeline Testing
Machine Learning in Practice - AnalysisIntermediateNew
Run a Pre-Deployment Fairness + Drift Audit on a Hiring Model
You receive a trained classifier (joblib), the training data sample, and a held-out 'next-month' evaluation set. Compute group fairness metrics (false-positive-rate gap, true-po…
- Fairness Metrics
- Drift Detection
- Bias Mitigation
Machine Learning in Practice - AnalysisIntermediateNew
Compare Kernel SVMs and Gradient Boosting on Imbalanced Tabular Data
You receive a 220,000-row anonymized loan-default dataset with mixed numeric and categorical features and a ~6% positive class. Train and evaluate (1) an RBF-kernel SVM with pro…
- Kernel Methods
- Gradient Boosting
- Model Selection
Machine Learning - CodeIntermediateNew
Build an Ensemble Strategy for Marketing-Mix Modelling
You receive 36 months of weekly marketing-spend and outcome data for 8 sample brands. Build a per-brand baseline gradient-boosting MMM model, then build two more base learners (…
- Ensemble Methods
- Stacking
- Time Series Cv
Machine Learning - ResearchIntermediateNew
Explore the Bias-Variance Trade-off on a Tabular Healthcare Cohort
You receive a 90,000-patient anonymized de-identified tabular dataset (demographics, labs, claims-derived features) and a binary 12-month-readmission outcome. Pick three model f…
- Bias Variance Tradeoff
- Regularization
- Model Selection
Machine Learning - AnalysisIntermediateNew
Audit BLEU vs. COMET on a Multilingual Customer-Support Corpus
You receive 600 source-translation-reference triples covering 6 languages (EN as source; ES/FR/DE/JA/PT-BR/HI as targets), each scored on adequacy and fluency (1-6) by 3 profess…
- Mt Evaluation
- Neural Mt
- Statistical Analysis
Machine Translation - CodeIntermediateNew
Few-Shot Defect Classifier for a Fast-Onboarding Industrial AI Vendor
You receive a multi-customer defect dataset (8 historical customers, 4-6 defect classes each). Treat 6 customers as the meta-training set and 2 as the held-out 'new customer' sc…
- Meta Learning
- Few Shot Learning
- Prototypical Networks
Meta-Learning, Transfer Learning, and Multi-Task Learning
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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