The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

This dataset contains sales transaction records used to create an interactive Excel Sales Performance Dashboard for business analytics practice.

It includes six columns capturing essential sales metrics such as date, region, product, quantity, sales revenue, and profit. The data is structured to help analysts and learners explore data visualization, PivotTable summarization, and dashboard design concepts in Excel.

The dataset was created for educational and demonstration purposes to help users:

1. Build dashboards that visualize total sales and profit trends
2. Identify top-performing products and high-profit regions
3. Practice Excel-based business analytics workflows

Columns: Date – Transaction date (daily sales record) Region – Geographic area of the sale (East, West, North, South) Product – Product category or item sold Sales – Total revenue generated from the sale (USD) Profit – Net profit made per transaction Quantity – Number of units sold

Typical uses include: Excel or Power BI dashboard projects PivotTable practice for business reporting Data cleaning and chart-building exercises Portfolio development for business analytics students Built and tested in Microsoft Excel using PivotTables, Charts, and Conditional Formatting.

Project Priority Matrix (Dynamic Excel Tool)

Overview

This dataset provides a dynamic Excel model for prioritizing projects based on Feasibility, Impact, and Size.
It visualizes project data on a Bubble Chart that updates automatically when new projects are added.

Use this tool to make data-driven prioritization decisions by identifying which projects are most feasible and high-impact.

Goal

Organizations often struggle to compare multiple initiatives objectively.
This matrix helps teams quickly determine which projects to pursue first by visualizing:

• Feasibility → How achievable a project is
• Impact → The potential benefit or value it delivers
• Size → The level of effort or resources required

How It Works

1. Each project is rated on a 1–10 scale for:
   • Feasibility
   • Impact
   • Size
2. The Excel file uses a Bubble Chart:
   • X-axis: Feasibility
   • Y-axis: Impact
   • Bubble size: Project Size
3. The chart automatically updates when new projects or scores are added.

Example (partial data):

Interpretation Guide

Excel Features

• Dynamic Bubble Chart (updates with new data)
• Named Ranges for auto-expanding data
• Optional Conditional Formatting
• Data Validation for consistent scoring

How to Use

1. Download and open Project_Priority_Matrix.xlsx.
2. Go to the Data sheet.
3. Add your project names and scores (1–10).
4. Watch the chart update instantly to reflect your data.

You can use this for: - Portfolio management
- Product or feature prioritization
- Strategy planning workshops

File Information

• File: Project_Priority_Matrix.xlsx
• Format: Excel (.xlsx)
• Version: 1.0
• Last Updated: October 2025

License

Free for personal and organizational use.
Attribution is appreciated if you share or adapt this file.

Author: [Asjad]
Contact: [m.asjad2000@gmail.com]
Compatible With: Microsoft Excel 2019+ / Office 365

This interactive sales dashboard is designed in Excel for B2C type of Businesses like Dmart, Walmart, Amazon, Shops & Supermarkets, etc. using Slicers, Pivot Tables & Pivot Chart.

Dashboard Overview

1. Sales dashboard ==> basically, it is designed for the B2C type of business. like Dmart, Walmart, Amazon, Shops & supermarkets, etc.
2. Slices ==> slices are used to drill down the data, on the basis of yearly, monthly, by sales type, and by mode of payment.
3. Total Sales/Total Profits ==> here is, the total sales, total profit, and profit percentage these all are combined into a monthly format and we can hide or unhide it to view it as individually or comparative.
4. Product Visual ==> the visual indicates product-wise sales for the selected period. Only 10 products are visualized at a glance, and you can scroll up & down to view other products in the list.
5. Daily Sales ==> It shows day-wise sales. (Area Chart)
6. Sales Type/Payment Mode ==> It shows sales percentage contribution based on the type of selling and mode of payment.
7. Top Product & Category ==> this is for the top-selling product and product category.
8. Category ==> the final one is the category-wise sales contribution.

Datasheets Overview

1. The dataset has the master data sheet or you can call it a catalog. It is added in the table form.
2. The first column is the product ID the list of items in this column is unique.
3. Then we have the product column instead of these two columns, we can manage with only one also but I kept it separate because sometimes product names can be the same, but some parameters will be different, like price, supplier, etc.
4. The next column is the category column, which is the product category. like cosmetics, foods, drinks, electronics, etc.
5. Then we have 4th column which is the unit of measure (UOM) you can update it also, based on the products you have.
6. And the last two columns are buying price and selling price, which means unit purchasing price and unit selling price.

Input Sheet

The first column is the date of Selling. The second column is the product ID. The third column is quantity. The fourth column is sales types, like direct selling, are purchased by a wholesaler or ordered online. The fifth column is a mode of payment, which is online or in cash. You can update these two as per requirements. The last one is a discount percentage. if you want to offer any discount, you can add it here.

Analysis Sheet: where all backend calculations are performed.

So, basically these are the four sheets mentioned above with different tasks.

However, a sales dashboard enables organizations to visualize their real-time sales data and boost productivity.

A dashboard is a very useful tool that brings together all the data in the forms of charts, graphs, statistics and many more visualizations which lead to data-driven and decision making.

Questions & Answers

1. What percentage of profit ratio of sales are displayed in the year 2021 and year 2022? ==> Total profit ratio of sales in the year 2021 is 19% with large sales of PRODUCT42, whereas profit ratio of sales for 2022 is 22% with large sales of PRODUCT30.
2. Which is the top product that have large number of sales in year 2021-2022? ==> The top product in the year 2021 is PRODUCT42 with the total sales of $12,798 whereas in the year 2022 the top product is PRODUCT30 with the total sales of $13,888.
3. In Area Chart which product is highly sold on 28th April 2022? ==> The large number of sales on 28th April 2022 is for PRODUCT14 with a 24% of profit ratio.
4. What is the sales type and payment mode present? ==> The sale type and payment modes show the sales percentage contribution based on the type of selling and mode of payment. Here, the sale types are Direct Sales with 52%, Online Sales with 33% and Wholesaler with 15%. Also, the payment modes are Online mode and Cash equally distributed with 50%.
5. In which month the direct sales are highest in the year 2022? ==> The highest direct sales can be easily identified which is designed by monthly format and it’s the November month where direct sales are highest with 28% as compared with other months.
6. Which payment mode is highly received in the year 2021 and year 2022? ==> The payments received in the year 2021 are the cash payments with 52% as compared with online transactions which are 48%. Also, the cash payment highly received is in the month of March, July and October with direct sales of 42%, Online with 45% and wholesaler with 13% with large sales of PRODUCT24. ==> The payments received in the year 2022 are the Online payments with 52% as compared with cash payments which are 48%. Also, the online payment highly received is in the month of Jan, Sept and December with direct sales of 45%, Online with 37% and whole...

This data publication is part of the 'P³-Petrophysical Property Database' project, which was developed within the EC funded project IMAGE (Integrated Methods for Advanced Geothermal Exploration, EU grant agreement No. 608553) and consists of a scientific paper, a full report on the database, the database as excel and .csv files and additional tables for a hierarchical classification of the petrography and stratigraphy of the investigated rock samples (see related references). This publication here provides a hierarchical interlinked stratigraphic classification according to the chronostratigraphical units of the international chronostratigraphic chart of the IUGS v2016/04 (Cohen et al. 2013, updated) according to international standardisation. As addition to this IUGS chart, which is also documented in GeoSciML, stratigraphic IDs and parent IDs were included to define the direct relationships between the stratigraphic terms. The P³ database aims at providing easily accessible, peer-reviewed information on physical rock properties relevant for geothermal exploration and reservoir characterization in one single compilation. Collected data include hydraulic, thermophysical and mechanical properties and, in addition, electrical resistivity and magnetic susceptibility. Each measured value is complemented by relevant meta-information such as the corresponding sample location, petrographic description, chronostratigraphic age and, most important, original citation. The original stratigraphic and petrographic descriptions are transferred to standardized catalogues following a hierarchical structure ensuring intercomparability for statistical analysis, of which the stratigraphic catalogue is presented here. These chronostratigraphic units are compiled to ensure that formations of a certain age are connected to the corresponding stratigraphic epoch, period or erathem. Thus, the chronostratigraphic units are directly correlated to each other by their stratigraphic ID and stratigraphic parent ID and can thus be used for interlinked data assessment of the petrophysical properties of samples of an according stratigraphic unit.

📊 Bank Transaction Analytics Dashboard – SQL + Excel

🔹 Overview

This project focuses on Bank Transaction Analysis using a combination of SQL scripts and Excel dashboards. The goal is to provide insights into customer spending patterns, payment modes, suspicious transactions, and overall financial trends.

The dataset and analysis files can help learners and professionals understand how SQL and Excel can be used together for business decision-making, customer behavior tracking, and data-driven insights.

🔹 Contents

The dataset includes the following resources:

📂 SQL Scripts:

Create & Insert tables

15 Basic Queries

15 Advanced Queries

📂 CSV File:

Bank Transaction Analytics.csv (main dataset)

📂 Excel Charts:

Pie, Bar, Column, Line, Doughnut charts

Final Interactive Dashboard

📂 Screenshots:

Query outputs, Charts, and Final Dashboard visualization

📂 PDF Reports:

Project Report

Dashboard Report

📄 README.md:

Complete documentation and step-by-step explanation

🔹 Key Insights

26–35 age group spent the most across categories.

Amazon identified as the top merchant.

NetBanking showed the highest share compared to POS/UPI.

Travel & Shopping emerged as dominant categories.

🔹 Applications

Detecting suspicious transactions.

Understanding customer behavior.

Identifying top merchants and categories.

Building business intelligence dashboards.

🔹 How to Use

Download the dataset and SQL scripts.

Run Bank_Transaction_Analytics.SQL to create and insert data.

Execute the queries (Basic + Advanced) for insights.

Open Excel files to explore interactive charts and dashboards.

Refer to Project Report PDF for documentation.

🔹 Author

👩‍💻 Created by: Prachi Singh

GitHub: Bank Transaction Analytics Dashboard(https://github.com/prachi-singh-ds/Bank-Transaction-Analytics-Dashboard)

⚡This project is a complete SQL + Excel integration case study and is suitable for Data Science, Business Analytics, and Data Engineering portfolios.

📊 Road Accident Data Analysis: Interactive Excel Dashboard 🚗

Excited to share my Kaggle project focusing on road accident data analysis. Leveraging Excel's power, I've developed an interactive dashboard offering comprehensive insights for safer roads.

Key Aspects:

Data Processing & Cleaning: Ensured data reliability through meticulous processing. KPIs: Primarily focused on Total Casualties, with detailed breakdowns for Fatal, Serious, Slight, and by Car type. Visualizations: Engaging charts - Doughnuts, Line, Bar, and Pie - offering a holistic view of accident trends. Interactivity: User-friendly features include Urban/Rural and Year filters for dynamic exploration. Unique Insights:

Monthly Trends: Line chart for a nuanced comparison of current vs. previous year casualties. Road Type Breakdown: Bar chart to showcase casualties distributed across different road types. Geospatial Analysis: Doughnut charts detailing casualties by location and area. Call for Collaboration: Seeking Kaggle community input for refinement and optimization. Let's collectively contribute to making our roads safer through data-driven insights!

DataAnalysis #RoadSafety #InteractiveDashboard #KaggleProject #Excel #DataVisualization #CollaborationOpportunity

Looking forward to your feedback and contributions! 🚀🌐