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...

Excel sheets in order: The sheet entitled “Hens Original Data” contains the results of an experiment conducted to study the response of laying hens during initial phase of egg production subjected to different intakes of dietary threonine. The sheet entitled “Simulated data & fitting values” contains the 10 simulated data sets that were generated using a standard procedure of random number generator. The predicted values obtained by the new three-parameter and conventional four-parameter logistic models were also appeared in this sheet. (XLSX)

Context

This file contains the stock information of Microsoft from 04/01/2015 to 04/01/2021

Content

This data was acquired in google sheets using the command 'GOOGLEFINANCE'

Inspiration

With this data you can do basic EDA and use predictive analysis.

Complete financial data including income statement, balance sheet, and cash flow for Microsoft Corporation as of Q4 2025

Complete financial data including income statement, balance sheet, and cash flow for MICROSOFT CORP as of Q4 2025

The global spreadsheet editor market is booming, projected to reach $130 billion by 2033 with a 10% CAGR. Discover key market trends, leading players (Microsoft, Google, LibreOffice), and regional growth insights in our comprehensive analysis. Explore the impact of cloud solutions, free vs. paid models, and future market potential.

Dataset description

As part of the project "RAAV - Rural Accessibility and Automated Vehicles" between the TU Vienna (Austria) and the EURAC institute (Bolzano, Italy), this file serves to summarise the results of a test of the PT-STA method in a comprehensible manner and to make them publicly available.

Context and methodology

An adaption of a classical STA accessibility analysis was formulated and the new method tested on a sample of over 100 individuals in Mühlwald, South Tyrol and over 100 individuals in Sooß, Lower Austria. The test is based on travel diaries, which have been attained in cooperation with and by interviewing said individuals.

To be as transparent as possible the data is provided in the Microsoft Excel format with all cell references. By doing this, we ensure that the data can also be used and adapted for other research. The travel diaries on which this research is based on can be accessed here: https://researchdata.tuwien.ac.at/records/hq7b7-xsa12

Technical details

The dataset contains one Microsoft Excel file containing multiple data sheets. All data from both regions, Mühlwald and Sooß were cumulated. In order to ensure data protection and anonymisation all names, addresses and coordinates of interviewed people, origins and destinations have been deleted from the dataset.

Other than Microsoft Excel, there is no additional software needed to investigate the data. The first datasheet gives an overview of abbreviations and data stored in each data sheet.

Muzumder A et al study on SARS-CoV-2 epidemic in India, excel sheet containing indian population data

This is a sample of CSV files that can be downloaded from Microsoft Teams after meetings. As MS Teams was used lately in delivering classes in schools and universities, it was important to follow the attendance of the students, therefore, this dataset can be used in writing the code that analyses the attendance of the students.

Discover the booming spreadsheet editor market! Explore key trends, growth forecasts (CAGR 8%), leading players (Microsoft, Google, Apple), and regional market shares in this comprehensive analysis. Learn how cloud-based solutions and advanced features are driving market expansion through 2033.

Dataset description

As part of the project "RAAV - Rural Accessibility and Automated Vehicles" between the TU Vienna (Austria) and the EURAC institute (Bolzano, Italy), this file serves to summarise the results of the application of the PT-STA method for separate public transport scenarios in a comprehensible manner and to make them publicly available.

Context and methodology

An adaption of a classical STA accessibility analysis was applied on a sample of over 100 individuals in Sooss, Lower Austria. Five different public transport scenarios based on a possible implication of automated vehicle technology were compared regarding their potential impact on accessibility for the local population.

To be as transparent as possible the data is provided in the Microsoft Excel format with all cell references. By doing this, we ensure that the data can also be used and adapted for other research.

Technical details

The dataset contains one Microsoft Excel file containing multiple data sheets. In order to ensure data protection and anonymisation all names, addresses and coordinates of interviewed people, origins and destinations have been deleted from the dataset.

Other than Microsoft Excel, there is no additional software needed to investigate the data. The first datasheet gives an overview of abbreviations and data stored in each data sheet.

The global spreadsheet editor market is booming, with a projected $50 billion valuation in 2025 and a 7% CAGR through 2033. Discover key drivers, restraints, and market trends impacting major players like Microsoft, Google, and Apple. Explore regional market share data and future growth projections in this comprehensive analysis.

Dataset description

As part of the project "RAAV - Rural Accessibility and Automated Vehicles" between the TU Vienna (Austria) and the EURAC institute (Bolzano, Italy), this file serves to summarise the results of the application of the PT-STA method for separate public transport scenarios in a comprehensible manner and to make them publicly available.

Context and methodology

An adaption of a classical STA accessibility analysis was applied on a sample of over 100 individuals in Mühlwald, South Tyrol. Five different public transport scenarios based on a possible implication of automated vehicle technology were compared regarding their potential impact on accessibility for the local population.

To be as transparent as possible the data is provided in the Microsoft Excel format with all cell references. By doing this, we ensure that the data can also be used and adapted for other research.

Technical details

The dataset contains one Microsoft Excel file containing multiple data sheets. In order to ensure data protection and anonymisation all names, addresses and coordinates of interviewed people, origins and destinations have been deleted from the dataset.

Other than Microsoft Excel, there is no additional software needed to investigate the data. The first datasheet gives an overview of abbreviations and data stored in each data sheet.

Microsoft Excel sheet with QC data from [69] used in Figs 5 and C in S1 File.

This file contains the complete dataset collected by the three experiments described in the companion paper, in Microsoft Excel (XLSX) format. The workbook contains a data keys sheet explaining any abbreviations, annotations, and labels used throughout the datafile, followed by a sheet for each of the experiments. The file has been verified to open in Microsoft Excel (https://products.office.com/excel) and Libre Office (https://www.libreoffice.org)

Antimicrobial resistance (AMR) is a silent pandemic that has claimed millions of lives, and resulted in long-term disabilities, limited treatment options, and high economic costs associated with the healthcare burden. Given the rising prevalence of AMR, which is expected to pose a challenge to current empirical antibiotic treatment strategies, we sought to summarize the available data on knowledge, attitudes, and practices regarding AMR in Ethiopia. Articles were searched in international electronic databases. Microsoft Excel spreadsheet and STATA software version 16 were used for data extraction and analysis, respectively. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis 2020 checklist was followed. The methodological quality of the studies included was assessed by the Joana Briggs Institute critical appraisal checklists. The random-effect meta-analysis model was used to estimate Der Simonian-Laird’s pooled effect. Statistical heterogeneity of the meta-analysis was checked through Higgins and Thompson’s I2 statistics and Cochran’s Q test. Publication bias was investigated by funnel plots, and the regression-based test of Egger for small study effects with a P value < 0.05 was considered to indicate potential reporting bias. In addition, sensitivity and subgroup meta-analyses were performed. Fourteen studies with a total of 4476 participants met the inclusion criteria. Overall, the pooled prevalence of good AMR knowledge was 51.53% [(95% confidence interval (CI): 37.85, 65.21), I2 = 99.0%, P

The dataset contains the results of developing alternative text for images using chatbots based on large language models. The study was carried out in April-June 2024. Microsoft Copilot, Google Gemini, and YandexGPT chatbots were used to generate 108 text descriptions for 12 images. Descriptions were generated by chatbots using keywords specified by a person. The experts then rated the resulting descriptions on a Likert scale (from 1 to 5). The data set is presented in a Microsoft Excel table on the “Data” sheet with the following fields: record number; image number; chatbot; image type (photo, logo); request date; list of keywords; number of keywords; length of keywords; time of compilation of keywords; generated descriptions; required length of descriptions; actual length of descriptions; description generation time; usefulness; reliability; completeness; accuracy; literacy. The “Images” sheet contains links to the original images. Alternative descriptions are presented in English.

The global Office Suite Tools market is projected for robust growth, with an estimated market size of USD 4689 million in 2025 and a projected Compound Annual Growth Rate (CAGR) of 5.8% from 2025 to 2033. This expansion is driven by several key factors, including the escalating demand for cloud-based solutions that offer enhanced collaboration and accessibility for remote and hybrid workforces. The ongoing digital transformation across industries, coupled with the increasing adoption of productivity tools by both businesses and individual users for efficient document creation, management, and sharing, underpins this positive market trajectory. Furthermore, the continuous innovation in features, such as advanced AI-powered writing assistants, real-time co-editing capabilities, and tighter integration with other business applications, is attracting new users and encouraging upgrades, further fueling market expansion. The growing reliance on digital platforms for everyday tasks in both professional and personal settings solidifies the essential nature of these tools, ensuring sustained demand. The market is segmented by application into "For Business" and "For Home Users," with businesses representing a significantly larger share due to their extensive requirements for enterprise-grade features and scalability. By type, cloud-based solutions are outpacing traditional web-based alternatives, driven by their inherent flexibility, cost-effectiveness, and the ability to access data from anywhere, on any device. While the market exhibits strong growth, certain restraints need to be acknowledged. These include concerns around data security and privacy, particularly with cloud-based offerings, and the initial investment costs associated with migrating to new or comprehensive office suite solutions for some organizations. However, the competitive landscape, featuring a wide array of established players like Microsoft Office Online, Google Drive, and Zoho Workplace, alongside emerging innovators such as Dropbox Paper and Smart Sheet, fosters a dynamic environment characterized by rapid feature development and increasing accessibility through freemium models, ultimately benefiting end-users. Here's a report description for Office Suite Tools, incorporating your specified requirements:

This in-depth report offers a comprehensive analysis of the global Office Suite Tools market, providing critical insights into its current landscape and future trajectory. Spanning a study period from 2019 to 2033, with a base year of 2025 and a forecast period of 2025-2033, this report delves into historical trends, market dynamics, and anticipated growth drivers. Our rigorous research methodology, encompassing both quantitative and qualitative analyses, ensures a robust understanding of this evolving sector. The estimated market size for office suite tools is projected to be in the hundreds of millions of dollars, with significant growth expected throughout the forecast period.

Overview:

This comprehensive dataset combines Microsoft Corporation's historical stock price data with its annual and quarterly financial statements. It provides a rich source of information for financial analysis, investment research, and data-driven decision-making.

Content:

This dataset comprises the following key components:

• Microsoft Stock Price Data: This section includes historical daily closing prices of Microsoft (MSFT) common stock. The dataset covers a significant time frame, making it suitable for long-term trend analysis and portfolio optimization.

• Annual Financial Statements:

Balance Sheets: Microsoft's annual balance sheets, offering insights into the company's financial position, assets, liabilities, and equity. Income Statements: Annual income statements presenting revenue, expenses, and profitability metrics. Cash Flow Statements: Annual cash flow statements providing details on operating, investing, and financing activities.

• Quarterly Financial Statements:

Balance Sheets: Microsoft's quarterly balance sheets for a more granular view of financial changes throughout the year. Income Statements: Quarterly income statements offering a closer look at revenue and expenses trends. Cash Flow Statements: Quarterly cash flow statements for insights into short-term financial dynamics.

• Use Cases:

Financial Analysis: Researchers and analysts can use this dataset to perform in-depth financial analysis, including ratio analysis, trend analysis, and performance benchmarking.

Investment Research: Investors can leverage this data to make informed investment decisions, assess risk, and evaluate Microsoft's financial health.

Portfolio Management: Portfolio managers can use historical stock price data to optimize their portfolios and monitor the performance of Microsoft within their holdings.

• Data Sources:

The financial data in this dataset is collected from the Yahoo Finance API, a reliable and widely-used source of financial data. The stock price data is specifically sourced from this API.

• Note on Data Quality:

Efforts have been made to ensure the accuracy and consistency of the data collected from the Yahoo Finance API. However, users are encouraged to verify the information independently for critical applications. As with any financial dataset, it's essential to exercise due diligence in analysis and decision-making.

Data_NNL.xlsx (Micrsoft Excel 2010) file contains two sheets. The first sheet has anonymized patient wise data of dengue deaths in 2017-18 that were reported in Myanmar. The second sheet in the file contains the codebook