A dataset I generated to showcase a sample set of user data for a fictional streaming service. This data is great for practicing SQL, Excel, Tableau, or Power BI.

1000 rows and 25 columns of connected data.

See below for column descriptions.

Enjoy :)

Dataset

This dataset was created by BunY12345

Contents

The Superstore Sales Data dataset, available in an Excel format as "Superstore.xlsx," is a comprehensive collection of sales and customer-related information from a retail superstore. This dataset comprises* three distinct tables*, each providing specific insights into the store's operations and customer interactions.

Warning: Large file size (over 1GB). Each monthly data set is large (over 4 million rows), but can be viewed in standard software such as Microsoft WordPad (save by right-clicking on the file name and selecting 'Save Target As', or equivalent on Mac OSX). It is then possible to select the required rows of data and copy and paste the information into another software application, such as a spreadsheet. Alternatively, add-ons to existing software, such as the Microsoft PowerPivot add-on for Excel, to handle larger data sets, can be used. The Microsoft PowerPivot add-on for Excel is available from Microsoft http://office.microsoft.com/en-gb/excel/download-power-pivot-HA101959985.aspx Once PowerPivot has been installed, to load the large files, please follow the instructions below. Note that it may take at least 20 to 30 minutes to load one monthly file. 1. Start Excel as normal 2. Click on the PowerPivot tab 3. Click on the PowerPivot Window icon (top left) 4. In the PowerPivot Window, click on the "From Other Sources" icon 5. In the Table Import Wizard e.g. scroll to the bottom and select Text File 6. Browse to the file you want to open and choose the file extension you require e.g. CSV Once the data has been imported you can view it in a spreadsheet. What does the data cover? General practice prescribing data is a list of all medicines, dressings and appliances that are prescribed and dispensed each month. A record will only be produced when this has occurred and there is no record for a zero total. For each practice in England, the following information is presented at presentation level for each medicine, dressing and appliance, (by presentation name): - the total number of items prescribed and dispensed - the total net ingredient cost - the total actual cost - the total quantity The data covers NHS prescriptions written in England and dispensed in the community in the UK. Prescriptions written in England but dispensed outside England are included. The data includes prescriptions written by GPs and other non-medical prescribers (such as nurses and pharmacists) who are attached to GP practices. GP practices are identified only by their national code, so an additional data file - linked to the first by the practice code - provides further detail in relation to the practice. Presentations are identified only by their BNF code, so an additional data file - linked to the first by the BNF code - provides the chemical name for that presentation.

Tabular dataset for data analysis and machine learning practice. The dataset is about the market and is usable for Power BI practice and data science.

🔍 Total Sales: Achieved $456,000 in revenue across 1,000 transactions, with an average transaction value of $456.00.

👥 Customer Demographics:

Average Age: 41.39 years Gender Distribution: 51% male, 49% female Most active age groups: 31-40 & 41-50 years 🏷️ Product Performance:

Top Categories: Electronics and Clothing led the sales, each contributing $160,000, followed by Beauty products with $140,000. Quantity Sold: Clothing topped the charts with 894 units sold. 📈 Sales Trends: Identified key sales peaks, especially in May 2023, indicating the success of targeted promotional strategies.

Why This Matters:

Understanding these metrics allows for better-targeted marketing, efficient inventory management, and strategic planning to capitalize on peak sales periods. This project demonstrates the power of data-driven decision-making in retail!

💡 Takeaway: Power BI continues to be a game-changer in visualizing and interpreting complex data, helping businesses to not just see numbers but to translate them into actionable insights.

I’m always looking forward to new challenges and projects that push my skills further. If you're interested in diving into the details or discussing data insights, feel free to reach out!

PowerBI #DataAnalysis #RetailSales #DataVisualization #BusinessIntelligence #DataDriven

Adventure Works 2022 dataset

How this Dataset is created?

On the official website the dataset is available over SQL server (localhost) and CSVs to be used via Power BI Desktop running on Virtual Lab (Virtaul Machine). As per first two steps of Importing data are executed in the virtual lab and then resultant Power BI tables are copied in CSVs. Added records till year 2022 as required.

How this Dataset may help you?

this dataset will be helpful in case you want to work offline with Adventure Works data in Power BI desktop in order to carry lab instructions as per training material on official website. The dataset is useful in case you want to work on Power BI desktop Sales Analysis example from Microsoft website PL 300 learning.

How to use this Dataset?

Download the CSV file(s) and import in Power BI desktop as tables. The CSVs are named as tables created after first two steps of importing data as mentioned in the PL-300 Microsoft Power BI Data Analyst exam lab.

Supermarket Sales Dashboard in Power BI:

Hello Everyone, I made this Sales Dashboard in Power BI with the Supermarket dataset provided by leanexcelsolutions.

Problem Statement : The goal of this Power BI Dashboard is to analyze the sales performance of a supermarket using the provided Sample Data enabling stakeholders to make informed business decisions.

I created the sales dashboard in Power BI by following these steps: -Import data to Power BI -Edit Data in Power Query Editor -Create Columns & measures -Create Visuals -Format Dashboard Background -Format Visuals

Report has multiple sections from where you can manage the data, like : • Report data can be sliced by year, month, payment mode and sale type. • Report has cards showing Total sales and profit. • Report has different charts showing sales vs profit, monthly sales and also top-selling products. • I have also included a Reset button at the top to clear all slicers.

Link to the Dataset : https://leanexcelsolutions.com/sales-dashboard-in-excel-power-bi/

Domain-Specific Dataset and Visualization Guide

This package contains 20 realistic datasets in CSV format across different industries, along with 20 text files suggesting visualization ideas. Each dataset includes about 300 rows of synthetic but domain-appropriate data. They are designed for data analysis, visualization practice, machine learning projects, and dashboard building.

What’s inside

• 20 CSV files, one for each domain:

  1. Education
  2. E-Commerce
  3. Healthcare
  4. Finance
  5. Retail
  6. Social Media
  7. Manufacturing
  8. Sports
  9. Transport
  10. Hospitality
  11. Telecom
  12. Banking
  13. Real Estate
  14. Gaming
  15. Agriculture
  16. Automobile
  17. Energy
  18. Insurance
  19. Government
  20. Entertainment

20 TXT files, each listing 10 relevant graphing options for the dataset.

MASTER_INDEX.csv, which summarizes all domains with their column names.

Use cases

• Practice data cleaning, exploration, and visualization in Excel, Tableau, Power BI, or Python.
• Build dashboards for specific industries.
• Train beginner-level machine learning models such as classification and regression.
• Use in classroom teaching or workshops as ready-made datasets.

Example

• Education dataset has columns like StudentName, Class, Subject, Marks, AttendancePercent. Suggested graphs: bar chart of average marks by subject, scatter plot of marks vs attendance percent, line chart of attendance over time.

• E-Commerce dataset has columns like OrderDate, Product, Category, Price, Quantity, Total. Suggested graphs: line chart of revenue trend, bar chart of revenue by category, pie chart of payment mode share.

About Datasets:

Domain : Sales Project: Coca Cola Sales Analysis Datasets: Power BI Dataset vF Dataset Type: Excel Data Dataset Size: 52k+ records

KPI's: 1. Analyze Profit Margins per Brand 2. Sales by Region 3. Price per unit 4. Operating Profit 5. Additional Analysis

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains Power Query, Q&A visual, Key influencers visual, map chart, matrix, dynamic timeline, dashboard, formatting, text box.

Hello Everyone, I made this Finance Dashboard in Power BI with the Finance Excel Workbook provided by Microsoft on their Website. Problem Statement The goal of this Power BI Dashboard is to analyze the financial performance of a company using the provided Microsoft Sample Data. To create a visually appealing dashboard that provides an overview of the company's financial metrics enabling stakeholders to make informed business decisions. Sections in the Report Report has multiple section's from where you can manage the data, like : • Report data can be sliced by Segments, Country and Year to show particular data. - Report Contain Two Navigation Page one is overview and other is sales dashboard page for better visualisation of data. - Report Contain all the important data. - Report Contain different chart and bar garph for different section .

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ISO 3166-1-alpha-2 English country names and code elements. This list states the country names (official short names in English) in alphabetical order as given in ISO 3166-1 and the corresponding ISO 3166-1-alpha-2 code elements.

Checking Zomato network analysis in Price_rating, Price_range, Has on_delivery, Avg_restaurants near by cities, No. of Cities, No.of Countries. Drive Blog

Data was imported from the BAK file found here into SQL Server, and then individual tables were exported as CSV. Jupyter Notebook containing the code used to clean the data can be found here

Version 6 has a some more cleaning and structuring that was noticed after importing in Power BI. Changes were made by adding code in python notebook to export new cleaned dataset, such as adding MonthNumber for sorting by month number, similar for WeekDayNumber.

Cleaning was done in python while also using SQL Server to quickly find things. Headers were added separately, ensuring no data loss.Data was cleaned for NaN, garbage values and other columns.

This synthetic dataset captures detailed participant engagement across various community programs and events throughout the year 2024. It's designed to simulate real-world volunteer and event participation data and is ideal for building dashboards, practicing data cleaning, or creating portfolio projects in tools like Tableau, Power BI, Excel or Google Data Studio (Looker).

This dataset contains retail sales records from a superstore, including detailed information on orders, products, categories, sales, discounts, profits, customers, and regions.

It is widely used for business intelligence, data visualization, and machine learning projects. With features such as order date, ship mode, customer segment, and geographic region, the dataset is excellent for:

Sales forecasting

Profitability analysis

Market basket analysis

Customer segmentation

Data visualization practice (Tableau, Power BI, Excel, Python, R)

Inspiration:

Great dataset for learning how to build dashboards.

Commonly used in case studies for predictive analytics and decision-making.

Source: Originally inspired by a sample dataset frequently used in Tableau training and BI case studies.

About Dataset:

Domain : Marketing Project: Marketing Campaign Insight Analysis Datasets: marketing_campaign Dataset Type: Excel data Dataset Size: 69k+ records

KPI's: 1. Accepted Campaign 2. Income and Spending by Day 3. Income and Spending by Year and Quarter 4. Purchases 5. Spending by Education 6. Complain by Year and Quarter

Process: 1. Understanding the problem 2. Data Collection 3. Data Cleaning 4. Exploring and analyzing the data 5. Interpreting the results

This data contains power query, pie chart, text box, scatter chart, dax, stacked column chart.

The following are documents that were used to build a mock database and data warehouse and sample analysis on the data warehouse. The mock company is a summer camp agency. The software that was used for this project was SQL, Excel, Visual Studio, and Power BI.

The contoso_TR.accdb dataset is a Microsoft Access relational database representing a localized version of the well-known Contoso retail business scenario, tailored for the Turkish market (TR). It provides a rich, realistic sample of sales, product, customer, and financial data that can be used for learning, reporting, and analytics purposes.

🧾 Dataset Description This dataset simulates the operations of Contoso Ltd., a fictitious retail company that sells electronic products and accessories through various sales channels across Turkey. The database is designed to support a wide range of data-driven tasks such as:

Data modeling and relationship design

SQL querying and data transformation

Business intelligence and reporting

Dashboard creation using Power BI or Excel

Training in Access VBA and macros

🌍 Localization Language: Turkish (column names and values are adapted)

Currency: Turkish Lira (₺)

Region: Turkey-specific location data (e.g., cities, regions, and stores)

Date format: gg.aa.yyyy (Turkish date format)

✅ Use Cases Practicing Access SQL queries

Creating forms and reports in Microsoft Access

Developing ETL pipelines using sample business data

Preparing Power BI dashboards with Turkish-language data

Learning how to normalize and relate data in a business context

📌 Notes The dataset is static and does not reflect real-time data.

No real customer information is included; all data is synthetic.

It is ideal for educational and demonstration purposes.

If you'd like, I can help you:

Design a Power BI report using this dataset

Convert it to SQL Server or another format

Write SQL queries to extract business insights

This project presents two interactive dashboards created using Power BI and Excel to analyze supplier performance and volume movement for a manufacturing business unit. The dashboards are designed for decision-makers to monitor purchasing efficiency, evaluate supplier performance, and identify tonnage trends over time.

📊 Dashboard 1: Supplier Overview - Shows supplier-level purchase data, strategy alignment, payment terms, and MSME type - Includes city-level mapping for geographic insights - Visual indicators for casting and machining status - KPIs like total suppliers, purchase volume, and strategy status distribution

📈 Dashboard 2: Volume Movement - Tracks monthly and yearly purchase volume and tonnage - Supplier and part family-level tonnage breakdown - Invoicable volume by business units - Commodity-wise split (Ferrous / Non-Ferrous) - Filterable by plant, supplier, and part families