Beginner
Travel
Travel bookings
Bookings, destinations, trip duration and booking channel.
ProseText
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Search starter datasets by topic, preview their columns and sample rows, or download a CSV to test ProseText Data Cleaner. All examples on this page are small synthetic samples created for practice.
Bookings, destinations, trip duration and booking channel.
Products, sales quantity, unit price and store-level revenue.
Titles, genres, release year, runtime and audience score.
Departments, quarterly revenue, costs and operating margin.
A fictional employee dataset for checking missing values and duplicates.
Transaction dates, account types, amounts and payment channels.
Fictional appointment data for date, status and service analysis.
Location-based pollutant readings for environmental profiling.
Temperature, humidity, rainfall and weather condition by day.
Menu items, quantities, order channels and order totals.
Teams, match outcomes, scores and venue details.
Room types, guest counts, check-in dates and nightly rates.
Fictional service request types, wards and resolution times.
Mixed data types for trying basic profiling and cleaning operations.
Satisfaction scores, response channels and respondent segments.
Routes, travel duration, passenger counts and transport modes.
Fictional animal profiles, intake dates and adoption status.
Point locations with latitude, longitude and site metadata.
Daily visits, sign-ups and conversion rates for trend analysis.
Use the ProseText sample dataset library to practise data cleaning, spreadsheet analysis, and data quality checks. Choose a sample CSV file, preview its columns, and download it to test the ProseText online data cleaner. These small example datasets are designed for learners, students, analysts, and teams who want to understand how to prepare structured data before reporting or analysis.
Sample tables help you practise common data preparation tasks such as finding missing values, identifying duplicate records, checking inconsistent categories, trimming extra spaces, and reviewing column formats. Download a CSV sample and upload it to a data cleaning tool to explore the workflow before working with your own files.
Browse sample data for retail sales, travel bookings, business performance, finance transactions, employee records, healthcare appointments, weather observations, air quality, customer surveys, transport, hospitality, sports, and website metrics. The examples support basic spreadsheet practice and introductory data analysis exercises.
Reliable analysis starts with well-prepared data. Review each sample for blank cells, unexpected values, duplicate rows, inconsistent text, and columns that may need a clearer data type. Use these practice files to learn a repeatable data quality checking process before creating charts, summaries, or reports.
A sample dataset is a small collection of example records arranged in rows and columns. It can be used to practise spreadsheet skills, data cleaning, data validation, and basic analysis without using real customer information.
Yes. Use the Download CSV button on any dataset card to save its sample data as a CSV file. The examples on this page are synthetic demonstration data created for practice.
Download a sample CSV, open the ProseText Data Cleaner, and upload the file. Review the available analysis and cleaning options, select the operations you need, and inspect the output before relying on it.
Depending on the sample, you can practise spotting missing values, repeated rows, blank fields, extra whitespace, inconsistent labels, and values that may need further validation. These examples are intended for learning and do not guarantee that every issue appears in every dataset.
No. The sample records on this page are synthetic examples for practice. They are not official statistics, verified public datasets, or real customer records. Check the source and licence of any external dataset before using it.
Yes. Start with a Beginner dataset to practise searching, previewing, downloading, and checking simple tables. Intermediate examples include additional numeric, date, or category fields for more detailed practice.