Sales Data Cleaning Practice Dataset — Brightcart Synthetic Orders (CSV)

Brightcart Sales Dataset — Data Cleaning and Analytics Practice

Practice working with realistic data-quality challenges using this synthetic sales dataset created for the Brightcart case study in Data Analytics with AI: From Raw Data to Business Insights.

The dataset represents order records from a fictional business and is designed to help beginners understand why real-world data often needs careful preparation before analysis.

Dataset overview

• File: raw_orders_messy.csv
• Records: 6,485 rows
• Columns: 11
• Coverage: October 2025 to September 2026
• Data type: Synthetic, fictional sales and order data

What's inside?

The dataset includes fields such as:

• Order and customer identifiers
• Order dates
• Regions and product categories
• Quantities and unit prices
• Discount percentages
• Revenue and cost
• Return status

What can you practise?

• Inspecting data structure and quality
• Finding duplicate records and missing values
• Identifying inconsistent dates and text formats
• Preparing data for analysis
• Exploring sales, gross profit, and gross margin
• Developing evidence-based business insights

Who is it for?

Students, beginner data analysts, Python learners, and anyone practising data preparation and exploratory data analysis.

Format: CSV

Use: Educational practice and experimentation

Important: This is synthetic data for learning purposes. It does not represent actual customers, transactions, or a real business. The dataset contains deliberately introduced patterns and data-quality issues, so results from it should not be treated as real-world business findings.