# Free Sample Datasets for Data Analytics Download ready-to-use CSV datasets curated for manufacturing, retail, supply chain, healthcare, banking, energy, and operations. Each file is lightweight, realistically structured, and designed to import straight into Infoveave — no setup required. Data disclaimer: These are synthetic datasets inspired by publicly available industry data patterns. They are not generated or owned by Infoveave — all values are representative samples created for demonstration and learning purposes only. No real organisations, individuals, or transactions are represented. Filter by industry All(12)Retail(2)Manufacturing(2)Supply Chain(2)Healthcare(1)Banking(2)Energy(1)Operations(2) Filter by use case All use casesEDADQ — Dirty DataML / SciPyTransformation Showing 12 datasets Retail.csv ### Retail Daily Sales 25k daily POS transactions across 20 stores, 5 regions, and 8 product categories over two years. Rich time-series structure for trend decomposition, regional comparisons, and seasonal pattern analysis. 25,000 rows9 columns\~1.6 MB EDA Sales AnalysisRevenue TrendsDiscount ImpactTime Series [Analyze with AI](/resources/sample-datasets/retail-daily-sales) Inspired by: [Kaggle · Store Sales – Time Series Forecasting](https://www.kaggle.com/competitions/store-sales-time-series-forecasting) Retail.csv ### E-Commerce Orders 50k online orders over two years with product categories, shipping methods, return flags, and order status. Includes intentional dirty data: inconsistent category casing and \~5% null shipping\_method values. 50,000 rows10 columns\~4.1 MB EDADQ — Dirty Data Order FulfilmentReturns AnalysisDirty DataNull Handling [Analyze with AI](/resources/sample-datasets/ecommerce-orders) Inspired by: [Kaggle · Brazilian E-Commerce (Olist)](https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce) Manufacturing.csv ### Manufacturing OEE 30k hourly shift records across 20 machines over 500 days. Availability, performance, quality sub-scores, and defect counts at the machine-shift level — ideal for pivot/aggregation to machine-level, line-level, or daily rollups. 30,000 rows11 columns\~1.9 MB TransformationEDA OEEShift AnalysisAggregationPivotDefect Tracking [Analyze with AI](/resources/sample-datasets/manufacturing-oee) Inspired by: [Kaggle · Manufacturing OEE Dataset](https://www.kaggle.com/datasets/podsyp/production-quality) Manufacturing.csv ### Product Quality Control 20k batch inspection records with intentional DQ issues: \~3% duplicate batch IDs, null defect\_type values, outlier defect\_counts, and non-standard qc\_result entries. Designed for data profiling and DQ rule validation. 20,000 rows10 columns\~1.3 MB DQ — Dirty Data Dirty DataDuplicatesNull HandlingOutliersData Profiling [Analyze with AI](/resources/sample-datasets/product-quality-control) Inspired by: [UCI ML Repository · Steel Plates Faults](https://archive.ics.uci.edu/dataset/198/steel+plates+faults) Supply Chain.csv ### Supply Chain Shipments 30k shipment records with order date, dispatch, expected and actual delivery, carrier, weight, freight cost, and on-time flag across 10 origin/destination city pairs. Suited for SLA monitoring and pivot by carrier × region. 30,000 rows12 columns\~3.0 MB EDATransformation On-Time DeliveryCarrier PerformanceFreight CostPivot [Analyze with AI](/resources/sample-datasets/supply-chain-shipments) Inspired by: [Kaggle · SCMS Delivery History](https://www.kaggle.com/datasets/usaid-assist/supply-chain-shipment-pricing-data) Supply Chain.csv ### Inventory Levels 15k daily SKU-level stock snapshots across 300 SKUs and 5 warehouses. Includes stock value and a below-reorder flag — wide structure designed for unpivoting, reshaping to trend series, and warehouse-level aggregation. 15,000 rows10 columns\~1.0 MB Transformation Stock LevelsReorder AnalysisReshapeUnpivotWide Format [Analyze with AI](/resources/sample-datasets/inventory-levels) Inspired by: [Kaggle · Supply Chain & Inventory Analytics](https://www.kaggle.com/datasets/harshsingh2209/supply-chain-analysis) Healthcare.csv ### Healthcare Admissions 25k hospital admission records with three deliberate DQ issues: mixed date formats (ISO/US/UK), \~8% null age\_group, and \~2% duplicate admission IDs. A realistic scenario for date standardisation and null-imputation workflows. 25,000 rows10 columns\~1.7 MB DQ — Dirty DataML / SciPy Dirty DataMixed DatesNull ImputationReadmission RiskICD Codes [Analyze with AI](/resources/sample-datasets/healthcare-admissions) Inspired by: [Kaggle · Hospital Patient Records](https://www.kaggle.com/datasets/nehaprabhavalkar/av-healthcare-analytics-ii) Operations.csv ### Call Centre Performance 20k daily agent-level records across 200 agents and 7 queue types. Handles, AHT, wait time, FCR, CSAT, and escalations — a clean multi-dimensional dataset for distribution analysis and agent benchmarking. 20,000 rows10 columns\~1.1 MB EDA Agent ProductivityCSATHandle TimeDistribution AnalysisQueue Analysis [Analyze with AI](/resources/sample-datasets/call-center-performance) Inspired by: [Kaggle · Call Centre Performance Data](https://www.kaggle.com/datasets/mhdzahier/call-center) Banking.csv ### Financial Transactions 100k bank transaction records with an imbalanced fraud flag (\~2%). DQ issues: \~5% null merchant\_category, \~2% duplicate transaction IDs, \~1% outlier amounts. Suitable for fraud detection ML and data quality remediation. 100,000 rows10 columns\~7.8 MB DQ — Dirty DataML / SciPy Fraud DetectionImbalanced ClassificationDirty DataDuplicatesOutliers [Analyze with AI](/resources/sample-datasets/financial-transactions) Inspired by: [Kaggle · PaySim Synthetic Financial Dataset](https://www.kaggle.com/datasets/ealaxi/paysim1) Banking.csv ### Customer Churn 20k customer records with tenure, contract type, charges, product count, internet type, and a churn flag correlated with behaviour features. Clean numeric features ideal for binary classification and scipy.stats correlation analysis. 20,000 rows11 columns\~1.3 MB ML / SciPy Binary ClassificationCorrelation AnalysisFeature EngineeringSciPyLogistic Regression [Analyze with AI](/resources/sample-datasets/customer-churn) Inspired by: [Kaggle · Telco Customer Churn](https://www.kaggle.com/datasets/blastchar/telco-customer-churn) Energy.csv ### Energy Consumption 20k daily meter readings across 100 meters and 7 site types. Includes peak/off-peak split, tariff type, renewable percentage, and estimated CO₂. Ideal for time-series aggregation by site type and sustainability metric analysis. 20,000 rows10 columns\~1.6 MB EDATransformation Time SeriesPivotCO₂ TrackingPeak LoadTariff Analysis [Analyze with AI](/resources/sample-datasets/energy-consumption) Inspired by: [UCI ML Repository · Individual Household Electric Power Consumption](https://archive.ics.uci.edu/dataset/235/individual+household+electric+power+consumption) Operations.csv ### Employee Attrition 15k employee records with department, role level, tenure, salary band, performance rating, overtime, satisfaction scores, and an attrition flag correlated with satisfaction and workload. Clean ML-ready dataset for classification and scipy.stats hypothesis testing. 15,000 rows12 columns\~1.0 MB ML / SciPyEDA Binary ClassificationClusteringSciPyHypothesis TestingHR Analytics [Analyze with AI](/resources/sample-datasets/employee-attrition) Inspired by: [Kaggle · IBM HR Analytics Employee Attrition](https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset) ## Curated External Datasets Large, real-world datasets from Kaggle, UCI, and open government portals. Download directly from the original source — ideal when you need millions of rows, live data, or specific domain coverage. Filter by use case All use casesEDADQ — Dirty DataML / SciPyTransformation Showing 12 external datasets EDATransformation ### NYC Yellow Taxi Trips (2023) Over 3M taxi trip records per month with pickup/dropoff times, locations, fares, tips, and payment type. A classic EDA dataset for temporal patterns, borough-level aggregation, and fare distribution analysis. \~3M / month rows19 columns\~500 MB/month [View on NYC TLC Open Data](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page) EDA ### Airbnb Listings (NYC / London / Paris) Detailed Airbnb listing data with 70+ features including price, room type, neighbourhood, availability, and host metrics. Ideal for geospatial EDA, price distribution analysis, and outlier detection. 50k–300k rows74 columns30–150 MB [View on Inside Airbnb](http://insideairbnb.com/get-the-data) EDATransformation ### Global Superstore (Tableau Sample) Classic multi-dimensional sales dataset with orders, returns, and people tables across 51 countries, 3 segments, and 3 product categories. Perfect for cross-join analysis and Tableau-style EDA. 10k rows24 columns3 MB [View on Kaggle · Tableau Sample Data](https://www.kaggle.com/datasets/vivek468/superstore-dataset-final) DQ — Dirty DataML / SciPy ### Home Credit Default Risk 7 interrelated tables with 120+ columns, significant nulls, and skewed distributions. A real-world multi-table DQ challenge: imputation, join deduplication, and type correction across 300k+ applications. 300k+ rows120 columns700 MB [View on Kaggle · Home Credit Group](https://www.kaggle.com/competitions/home-credit-default-risk/data) DQ — Dirty DataEDA ### US Accidents (2016–2023) 2.8M accident records with real-world DQ issues: missing weather columns, inconsistent city names, duplicate incidents, and mixed coordinate formats. Good for null-handling and text standardisation. 2.8M rows46 columns1.07 GB [View on Kaggle · Sobhan Moosavi](https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents) DQ — Dirty DataML / SciPy ### Adult Income — UCI Census dataset where missing values are encoded as '?' strings — a realistic dirty data pattern. Used for DQ standardisation and binary income classification (>50K). 48k rows with 15 mixed-type features. 48k rows15 columns5 MB [View on UCI ML Repository](https://archive.ics.uci.edu/dataset/2/adult) ML / SciPy ### House Prices — Advanced Regression 79 explanatory variables describing residential homes in Ames, Iowa. Numerical, ordinal, and nominal features with non-normal distributions. Ideal for scipy.stats normality tests and regression modelling. 1.5k rows79 columns1 MB [View on Kaggle Competition](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data) ML / SciPy ### Credit Card Fraud Detection 284k European credit card transactions with 28 PCA-transformed features and a highly imbalanced fraud label (0.17%). Perfect for anomaly detection, precision-recall optimisation, and scipy imbalanced-class statistics. 284k rows30 columns150 MB [View on Kaggle · ULB](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud) ML / SciPy ### Wine Quality — UCI 6.5k red and white wine samples with 11 physiochemical input features and a quality score. Well-suited for scipy.stats correlation matrices, ANOVA, multi-class classification, and feature importance analysis. 6.5k rows12 columns2 MB [View on UCI ML Repository](https://archive.ics.uci.edu/dataset/186/wine+quality) TransformationEDA ### World Bank Development Indicators 1,400+ indicators across 200+ countries from 1960 to present, in wide format (one column per year). A classic unpivot exercise to convert to long format for time-series analysis. \~400k (long) rows65+120 MB [View on World Bank Open Data](https://databank.worldbank.org/source/world-development-indicators) TransformationEDA ### COVID-19 Global Cases — JHU Johns Hopkins University time-series dataset with one column per date — the canonical wide-to-long transformation exercise. Confirmed cases, deaths, and recoveries across 200+ countries. \~100k (long) rows1 per date50 MB [View on GitHub · CSSEGISandData](https://github.com/CSSEGISandData/COVID-19) TransformationEDA ### Chicago Crime Data (2001–Present) 7.7M reported crime incidents with location, type, arrest flag, and date. Good for group-by aggregation, reshape exercises, and EDA at scale. Requires joining with community-area and district lookup tables. 7.7M rows22 columns1.6 GB [View on Chicago Data Portal](https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-Present/ijzp-q8t2) ## Frequently Asked Questions What are these sample datasets and where do they come from? How do I import a CSV dataset into Infoveave? Can I use these datasets to test Infoveave's Fovea AI assistant? Are these datasets suitable for building demo dashboards? Can I request a sample dataset for a different industry or use case? What is the file format and are there any size restrictions? Ready to see Infoveave in action? 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