AI & Data
Statistics & Data Science Methods Challenges
Statistics & Data Science Methods challenges put you inside the work of drawing trustworthy conclusions from data. You'll build Statistics Fundamentals and Statistical Analysis, run Exploratory Data Analysis, Hypothesis Testing, Confidence Intervals, and Linear Regression, and design clean Sampling Methods.
From there you'll handle the harder edges — Bayesian methods, Causal inference, A/B testing with statistical significance, Monte Carlo Simulation, and Uncertainty Quantification — applying Experimental design the way data scientists actually do. Each challenge you solve earns a verified credential you can share with recruiters.
Recommended Challenges
· Exploratory Data Analysis Clear- All
- Data Analysis
- Experimental design
- Simulation
- Exploratory Data Analysis
- Statistical Analysis
- Uncertainty Quantification
- Logistic regression
- Cost Modeling
- Hypothesis Testing
- Monte Carlo Simulation
- A/B testing with statistical significance
- Linear Regression
- Time series basics
- Bayesian methods
- Causal inference
- Sampling Methods
- AnalysisIntermediateNew
Design an Electronic Health Record Data-Quality Audit
Stand up a Python (pandas + DuckDB) audit notebook ingesting the 14M-record extract. Define and run quality checks across four dimensions: completeness (required-field missingne…
- Health Informatics
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- Snomed Ct
Computational Biology and Health Informatics - AnalysisBeginnerNew
Mine Association Rules for a Grocery Retailer's Promo Strategy
You receive 6 months of basket-level transaction data (around 22 million baskets, around 18,000 SKUs) plus a category taxonomy. Run association-rule mining (Apriori or FP-Growth…
- Association Rules
- Market Basket Analysis
- Apriori
Data Mining and Knowledge Discovery - 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 - AnalysisBeginnerNew
Differential Expression Pipeline for an RNA-Seq Drug Discovery Run
Build a Snakemake pipeline running: fastp trimming, salmon quantification against a provided GENCODE reference, tximport for gene-level summarization, DESeq2 differential testin…
- Bioinformatics
- Computational Genomics
- Differential Expression
Computational Biology and Health Informatics Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- 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 - AnalysisFoundationalNew
Cluster Climate-Tech SMB Customers for a Growth Team
You receive a CSV with company size, industry sub-vertical, country, product features adopted, monthly active users, and lifetime value. Standardize features, decide on a cluste…
- Unsupervised Learning
- Clustering
- Dimensionality Reduction
Machine Learning (Undergraduate) - AnalysisBeginnerNew
Build a Public Open-Data Dashboard for Urban Mobility
Pull the city's open-data cyclist-collision dataset (10 years of incidents, geocoded). Define a clear before/after window around the protected-lane rollout, control for traffic-…
- Exploratory Data Analysis
- Data Wrangling
- Geospatial Analysis
Applied Data Analysis and Practical Data Science - 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.
- AnalysisBeginnerNew
Build a Reproducible Pricing Analysis for a DTC Skincare Brand
You receive 24 months of order-line data (around 480,000 lines), a Shopify-style customer export, and a discount-code log. Build a Python pipeline that produces: SKU-level price…
- Data Wrangling
- Exploratory Data Analysis
- Cohort Analysis
Applied Data Analysis and Practical Data Science - 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 - ResearchBeginnerNew
Drug-Repurposing Candidate Screen with Embedding Similarity
You receive (1) a list of 15 known therapeutic candidates (SMILES + ChEMBL identifiers) for a single rare disease, (2) a database of about 4,500 marketed drugs (SMILES + ATC cod…
- Molecular Embeddings
- Similarity Search
- Transfer Learning
Machine Learning for Healthcare and Biomedicine
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.
Industry teams behind a decade of practitioner briefs
Hiring from this pool?
Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.