Dataset

This dataset was created by Chris Chua

Contents

Sales Data Description This dataset represents synthetic sales data generated for practice purposes only. It is not real-time or based on actual business operations, and should be used solely for educational or testing purposes. The dataset contains information that simulates sales transactions across different products, regions, and customers. Each row represents an individual sale event with various details associated with it.

Columns in the Dataset

1. Product_ID: Unique identifier for each product sold. Randomly generated for practice purposes.
2. Sale_Date: The date when the sale occurred. Randomly selected from the year 2023.
3. Sales_Rep: The sales representative responsible for the transaction. The dataset includes five random sales representatives (Alice, Bob, Charlie, David, Eve).
4. Region: The region where the sale took place. The possible regions are North, South, East, and West.
5. Sales_Amount: The total sales amount for the transaction, including discounts if any. Values range from 100 to 10,000 (in currency units).
6. Quantity_Sold: The number of units sold in that transaction, randomly generated between 1 and 50.
7. Product_Category: The category of the product sold. Categories include Electronics, Furniture, Clothing, and Food.
8. Unit_Cost: The cost per unit of the product sold, randomly generated between 50 and 5000 currency units.
9. Unit_Price: The selling price per unit of the product, calculated to be higher than the unit cost.
10. Customer_Type: Indicates whether the customer is a New or Returning customer.
11. Discount: The discount applied to the sale, randomly chosen between 0% and 30%.
12. Payment_Method: The method of payment used by the customer (e.g., Credit Card, Cash, Bank Transfer).
13. Sales_Channel: The channel through which the sale occurred. Either Online or Retail.
14. Region_and_Sales_Rep: A combined column that pairs the region and sales representative for easier tracking.

Disclaimer

Please note: This data was randomly generated and is intended solely for practice, learning, or testing. It does not reflect real-world sales, customers, or businesses, and should not be considered reliable for any real-time analysis or decision-making.

1) Data Introduction • The Sample Sales Data is a retail sales dataset of 2,823 orders and 25 columns that includes a variety of sales-related data, including order numbers, product information, quantity, unit price, sales, order date, order status, customer and delivery information.

2) Data Utilization (1) Sample Sales Data has characteristics that: • This dataset consists of numerical (sales, quantity, unit price, etc.), categorical (product, country, city, customer name, transaction size, etc.), and date (order date) variables, with missing values in some columns (STATE, ADDRESSLINE2, POSTALCODE, etc.). (2) Sample Sales Data can be used to: • Analysis of sales trends and performance by product: Key variables such as order date, product line, and country can be used to visualize and analyze monthly and yearly sales trends, the proportion of sales by product line, and top sales by country and region. • Segmentation and marketing strategies: Segmentation of customer groups based on customer information, transaction size, and regional data, and use them to design targeted marketing and customized promotion strategies.

Dataset

This dataset was created by Lirik Sadiku

Contents

Sample Sales Data, Order Info, Sales, Customer, Shipping, etc., Used for Segmentation, Customer Analytics, Clustering and More. Inspired for retail analytics. This was originally used for Pentaho DI Kettle, But I found the set could be useful for Sales Simulation training.

Originally Written by María Carina Roldán, Pentaho Community Member, BI consultant (Assert Solutions), Argentina. This work is licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License. Modified by Gus Segura June 2014.

This dataset is based on the Sample Leads Dataset and is intended to allow some simple filtering by lead source. I had modified this dataset to support an upcoming Towards Data Science article walking through the process. Link to be shared once published.

This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

1) Data Introduction • The Power BI Sample Data is a financial sample dataset provided for Power BI practice and data visualization exercises that includes a variety of financial metrics and transaction information, including sales, profits, and expenses.

2) Data Utilization (1) Power BI Sample Data has characteristics that: • This dataset consists of numerical and categorical variables such as transaction date, region, product category, sales, profit, and cost, optimized for aggregation, analysis, and visualization. (2) Power BI Sample Data can be used to: • Revenue and Revenue Analysis: Analyze sales and profit data by region, product, and period to understand business performance and trends. • Power BI Dashboard Practice: Utilize a variety of financial metrics and transaction data to design and practice dashboards, reports, visualization charts, and more directly at Power BI.

The purpose of this fictional sales dataset is to provide data for Data Analysis practice. The 3 tables must be joined before one can analyze the data.

This fictional data set consists of 3 tables: 1. Customer dimension (history preserving) 2. Product dimension (history preserving) 3. Sales Transactions

The Customer Dimension dataset includes unique customer IDs, addresses, ages, and indicators of current records, with effective start and end dates for each customer.

The Product Dimension dataset details unique product IDs, names, prices, and their validity periods, along with indicators of current price records.

The Sales Transactions dataset captures sales activities with unique order IDs, product IDs, customer IDs, quantities sold, and order dates. Together, these datasets offer a comprehensive view of customer demographics, product pricing history, and sales transactions.

Standard error reference tables for the Retail Sales Index in Great Britain.

The Online Sales Dataset provides a detailed overview of global online sales transactions across various product categories. It includes transaction details such as order ID, date, product category, product name, quantity, unit price, total price, region, and payment method.

Dataset

This dataset was created by Murat Mutlu

Released under Data files © Original Authors

Contents

The given dataset appears to be a sales dataset containing information about different orders. Here is a description of the data:

1. Row ID: An identifier for each row in the dataset.
2. Order ID: Unique identifier for each order.
3. Order Date: The date when the order was placed.
4. Ship Date: The date when the order was shipped.
5. Ship Mode: The mode of shipping chosen for the order.
6. Customer ID: Unique identifier for each customer.
7. Customer Name: Name of the customer who placed the order.
8. Segment: The segment to which the customer belongs (e.g., consumer, corporate).
9. Country: The country where the order was placed (in this case, United States).
10. City: The city where the order was placed.
11. State: The state where the order was placed.
12. Postal Code: The postal code associated with the order's location.
13. Region: The region of the country where the order was placed.
14. Product ID: Unique identifier for each product.
15. Category: The category to which the product belongs (e.g., furniture, office supplies).
16. Sub-Category: The sub-category to which the product belongs (e.g., bookcases, chairs).
17. Product Name: The name of the product.
18. Cost: The cost of the product.
19. Price: The price at which the product was sold.
20. Profit: The profit made from the sale of the product.
21. Quantity: The quantity of the product ordered.
22. Sales: The total sales generated from the order (quantity multiplied by price).

The dataset provides detailed information about each order, including customer details, product details, sales information, and shipping information. It can be used to analyze various aspects of the sales data, such as profitability, customer segments, product categories, and regional sales performance.

The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

The sample sales record is a comprehensive table that provides information on the sales performance of various products across different regions of the world. The table contains several columns, each of which captures critical information about the sales of the products. The first column is the Region column, which indicates the geographical region where the sales took place. The second column is the Country column, which provides information on the specific country where the sales occurred. The third column is the Item Type column, which identifies the type of product that was sold. The fourth column is the Sales Channel column, which indicates the channel used to sell the product, such as online, retail, or wholesale. The fifth column is the Order Priority column, which ranks the importance of the order, such as urgent or normal. The sixth column is the Order Date column, which captures the date when the order was placed. The seventh column is the Order ID column, which is a unique identifier for each order. The eighth column is the Ship Date column, which captures the date when the product was shipped. The ninth column is the Units Sold column, which indicates the number of units sold for each order. The tenth column is the Unit Price column, which captures the price of each unit sold. The eleventh column is the Unit Cost column, which provides information on the cost of producing each unit. The twelfth column is the Total Revenue column, which indicates the total revenue generated from the sales. The thirteenth column is the Total Cost column, which captures the total cost of producing and selling the product. The final column is the Total Profit column, which provides information on the total profit generated from the sales. By analyzing the data in the sample sales record, businesses can gain valuable insights into their sales performance across different regions and identify areas for improvement.

A structured dataset of over three months of sales transactions from three supermarket branches. It includes attributes such as invoice ID, branch, city, customer type, gender, product line, unit price, quantity, total, tax, payment method, and gross income. Designed for predictive analytics, sales forecasting, and customer behavior analysis.

Dirty Retail Store Sales Dataset

Overview

The Dirty Retail Store Sales dataset contains 12,575 rows of synthetic data representing sales transactions from a retail store. The dataset includes eight product categories with 25 items per category, each having static prices. It is designed to simulate real-world sales data, including intentional "dirtiness" such as missing or inconsistent values. This dataset is suitable for practicing data cleaning, exploratory data analysis (EDA), and feature engineering.

File Information

• File Name: retail_store_sales.csv
• Number of Rows: 12,575
• Number of Columns: 11

Columns Description

Categories and Items

The dataset includes the following categories, each containing 25 items with corresponding codes, names, and static prices:

Electric Household Essentials

Furniture

This dataset is generated for the purpose of analyzing furniture sales data using multiple regression techniques. It contains 2,500 rows with 15 columns, including 7 numerical columns and 7 categorical columns, along with a target variable (revenue) which represents the total revenue generated from furniture sales. The dataset captures various aspects of furniture sales, such as pricing, cost, sales volume, discount percentage, inventory levels, delivery time, and different categorical attributes like furniture type, material, color, and store location.

Guys please upload your notebook of this dataset so that others can also learn from your work

Dataset

This dataset was created by selvam mts

Released under MIT

Contents

This is a dataset downloaded off excelbianalytics.com created off of random VBA logic. I recently performed an extensive exploratory data analysis on it and I included new columns to it, namely: Unit margin, Order year, Order month, Order weekday and Order_Ship_Days which I think can help with analysis on the data. I shared it because I thought it was a great dataset to practice analytical processes on for newbies like myself.