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.

Excel tables include all values used to generate graphs.

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

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

These data correspond to the main figures of the manuscript titled, "TCF-1-dependent and -independent restriction of the memory fate of CD8+ T cells enforced by BLIMP1."

Within the Figure_1.zip file, files include raw qPCR data with calculated delta Ct values, raw MFI values for in vitro stimulated WT and KO CD8+ T cells corresponding to Figure 1E and F, and fcs files of flow data presented in figures 1 E and F.

Within the Figure_2.zip file, files include the raw fcs files corresponding to panels A, C, and D. Sample information pertaining to each panel is provided in an excel file enumerating the cell culture conditions and genotypes of each sample. An excel file containing the raw numerical data of percent TCF1 positive for each sample in panel 2B is also provided.

Within the Figure_3.zip file, files include the raw fcs files corresponding to the representative plots in panels B, C, E, H, and J. Excel files containing the raw numerical data for graphs in panels B, C, D, F, G, H, and K are also included.

Within the Figure_4.zip file, files include the raw fcs files corresponding to the representative plots in panel G. An Excel file containing the raw numerical data for graphs in panel G. The genomic data have been separately uploaded to the NCBI GEO database.

Within the Figure_5.zip file, files include the raw fcs files corresponding to the representative plots in panels A, B, E, and G. Excel files containing the raw numerical data for graphs in panels A, B, C, E, F, G, H, and I are also included.

Within the Figure_6.zip file, files include the raw fcs files corresponding to the representative plots in panels A, B, and E. Excel files containing the raw numerical data for graphs in panels A, B, C, F, and H.

Within the Figure_7.zip file, files include the raw fcs files corresponding to the representative plots in panel A. Excel files containing the raw numerical data for graphs in panels B, F, and H are also included. The genomic data have been separately uploaded to the NCBI GEO database.

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...

Industrial Knowledge Graph Platform Market Outlook

Based on our latest research, the global Industrial Knowledge Graph Platform market size was valued at USD 1.23 billion in 2024, with a robust compound annual growth rate (CAGR) of 25.8% expected through the forecast period. With this trajectory, the market is projected to reach USD 9.08 billion by 2033. This exponential growth is fueled by the surge in industrial digitalization, the increasing need for contextual data integration, and the adoption of artificial intelligence (AI) and machine learning (ML) across industrial sectors. The market’s rapid expansion is underpinned by the critical role that knowledge graph platforms play in unifying disparate data sources, driving operational efficiency, and enabling advanced analytics for enterprise decision-making.

One of the primary growth drivers for the Industrial Knowledge Graph Platform market is the escalating demand for real-time, context-rich insights across industrial operations. As industries such as manufacturing, energy, and automotive embrace Industry 4.0 principles, the volume and complexity of data generated from interconnected devices and systems have increased dramatically. Knowledge graph platforms excel at integrating structured and unstructured data from diverse sources, enabling organizations to create a comprehensive, interconnected view of their assets, processes, and supply chains. This capability is crucial for enhancing operational transparency, optimizing resource allocation, and supporting predictive analytics, which collectively contribute to improved productivity and reduced downtime.

Another key factor propelling market growth is the widespread adoption of AI and ML technologies within industrial environments. Industrial knowledge graph platforms serve as foundational infrastructure for advanced AI applications by providing a semantic layer that contextualizes data relationships. This semantic enrichment empowers AI-driven solutions to deliver more accurate predictions, uncover hidden patterns, and automate complex decision-making processes. As organizations strive to achieve greater agility and resilience in the face of global supply chain disruptions and evolving regulatory requirements, knowledge graph platforms are increasingly seen as indispensable tools for digital transformation and competitive differentiation.

Furthermore, the growing emphasis on asset management, risk mitigation, and process optimization is fueling the adoption of industrial knowledge graph platforms. These platforms facilitate holistic visibility into asset lifecycles, maintenance schedules, and operational risks by connecting siloed data repositories and enabling cross-domain analytics. Industries such as oil & gas, pharmaceuticals, and chemicals, which operate in highly regulated environments, benefit significantly from the ability to trace data lineage, ensure compliance, and proactively manage risks. The integration of knowledge graphs with existing enterprise systems, including ERP, MES, and SCADA, further enhances their value proposition by streamlining workflows and supporting real-time decision-making.

Regionally, North America leads the global market, driven by early technology adoption, strong presence of key vendors, and significant investments in industrial IoT and AI initiatives. Europe follows closely, supported by robust manufacturing and automotive sectors, as well as stringent regulatory standards that encourage data integration and transparency. The Asia Pacific region is witnessing the fastest growth, propelled by rapid industrialization, government-led digitalization programs, and the proliferation of smart manufacturing initiatives in countries such as China, Japan, and South Korea. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as local industries increasingly recognize the value of knowledge graph platforms for operational excellence and risk management.

Component Analysis

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Performed in-depth analysis of Myntra's e-commerce data using Excel to identify sales trends, customer behavior, and performance metrics. Leveraged advanced Excel functionalities, including pivot tables, charts, conditional formatting, and data cleaning techniques, to derive actionable insights and create visually compelling reports.

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.

This MS Excel data has been processed into line graphs to create time series line graphs and data tables which give insight into changing physiochemical water quality characteristics and influences. The study sets out to determine if climate change has had an influence on physiochemical water quality characteristics both within and between the Breede and Olifants estuaries over a nine year monitoring period. The data represents changes and comparisons between salinity, temperature and rainfall within and between the Olifants and Breede river estuaries in the Wester Cape Province of South Africa.

According to Cognitive Market Research, the global Graph Database market size was USD 7.3 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 20.2% from 2024 to 2031. Market Dynamics of Graph Database Market

Key Drivers for Graph Database Market

Increasing demand for solutions with the capability to process low-latency queries-One of the main reasons the Graph Database market is extensively being used all over the globe, to the extent that numerous legacy database providers are endeavoring to assimilate graph database schemas into their main relational database infrastructures. Whereas, in theory, the strategy might save money, it might degrade and slow down the performance of queries run beside the database. A graph database is altering traditional brick-and-mortar trades into digital business powerhouses in terms of digital business activities.
Growing usage of graph database technology to drive the Graph Database market's expansion in the years ahead.

Key Restraints for Graph Database Market

Complex programming and standardization pose a serious threat to the Graph Database industry.
The market also faces significant difficulties related to low-cost clusters.

Introduction of the Graph Database Market

The graph database market has experienced significant growth due to the increasing need for efficient data management and complex relationship mapping in various industries. Unlike traditional relational databases, graph databases excel in handling interconnected data, making them ideal for applications such as social networks, fraud detection, recommendation engines, and supply chain management. Key drivers of this market include the rising adoption of big data analytics, advancements in artificial intelligence, and the proliferation of connected devices. Leading players, such as Neo4j, Amazon Web Services, and Microsoft, continue to innovate, offering scalable and robust graph database solutions. The growing demand for real-time, low-latency data processing capabilities further propels the market's expansion.

This data set was derived from experimental and numerical studies of sediment transport and the topographic evolution of a coupled river and rive-plume system. Data files are Microsoft Excel format and include data and graphs of experimental topographic cross sections, centerline profiles and numerical model runs used to produce the figures in Chatanantavet and Lamb, 2014. Funding was provided by NSF grant(s): OCE12-33685.

his project involves the creation of an interactive Excel dashboard for SwiftAuto Traders to analyze and visualize car sales data. The dashboard includes several visualizations to provide insights into car sales, profits, and performance across different models and manufacturers. The project makes use of various charts and slicers in Excel for the analysis.

Objective: The primary goal of this project is to showcase the ability to manipulate and visualize car sales data effectively using Excel. The dashboard aims to provide:

Profit and Sales Analysis for each dealer. Sales Performance across various car models and manufacturers. Resale Value Analysis comparing prices and resale values. Insights into Retention Percentage by car models. Files in this Project: Car_Sales_Kaggle_DV0130EN_Lab3_Start.xlsx: The original dataset used to create the dashboard. dashboards.xlsx: The final Excel file that contains the complete dashboard with interactive charts and slicers. Key Visualizations: Average Price and Year Resale Value: A bar chart comparing the average price and resale value of various car models. Power Performance Factor: A column chart displaying the performance across different car models. Unit Sales by Model: A donut chart showcasing unit sales by car model. Retention Percentage: A pie chart illustrating customer retention by car model. Tools Used: Microsoft Excel for creating and organizing the visualizations and dashboard. Excel Slicers for interactive filtering. Charts: Bar charts, pie charts, column charts, and sunburst charts. How to Use: Download the Dataset: You can download the Car_Sales_Kaggle_DV0130EN_Lab3_Start.xlsx file from Kaggle and follow the steps to create a similar dashboard in Excel. Open the Dashboard: The dashboards.xlsx file contains the final version of the dashboard. Simply open it in Excel and start exploring the interactive charts and slicers.

📊 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.

This dataset contains in-air hand-written numbers and shapes data used in the paper:B. Alwaely and C. Abhayaratne, "Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition," in IEEE Access, vol. 7, pp. 159661-159673, 2019, doi: 10.1109/ACCESS.2019.2950643.The dataset contains the following:-Readme.txt- InAirNumberShapeDataset.zip containing-Number Folder (With 2 sub folders for Matlab and Excel)-Shapes Folder (With 2 sub folders for Matlab and Excel)The datasets include the in-air drawn number and shape hand movement path captured by a Kinect sensor. The number sub dataset includes 500 instances per each number 0 to 9, resulting in a total of 5000 number data instances. Similarly, the shape sub dataset also includes 500 instances per each shape for 10 different arbitrary 2D shapes, resulting in a total of 5000 shape instances. The dataset provides X, Y, Z coordinates of the hand movement path data in Matlab (M-file) and Excel formats and their corresponding labels.This dataset creation has received The University of Sheffield ethics approval under application #023005 granted on 19/10/2018.

Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?:

This data set contains all the experimental raw data, analysis and source files for the final figures reported in the manuscript: "Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?". It is divided into five (1-5) zipped folders, named as the technique used to obtain the data. Each of them, where applicable, consists of three different subfolders (raw data, analysed data, final graph). Read below for more details.

1) ConfocalMicroscopy

1a) Raw_Data: the raw images are reported as .dat and .tif formats, divided into folders (according to date first yymmdd, and within the same day according to composition). Each folder contains a .txt file reporting the experimental details

1b) GUVs_Statistics - GUVs_Statistics.txt explains how we generated the bar plot shown in Fig. 1E

1c) Final_Graph - Figure_1B_1D.png is the figure representing figure 1B and 1D - Figure1E_%ofGUVswithCaMAdsorbptions.csv is the source file x-y of the bar plot shown in figure 1E (% of GUVs which showed adsorption of CaM over the total amount of measured GUVs) - Where_To_Find_Representative_Images.txt states the folders where the raw images chosen for figure 1 can be found

2) FCS 2a) Raw_Data: - 1_points: .ptu files - 2_points: .ht3 files - Raw_Data_Description.docx which compositions and conditions correspond to which point in the two data sets 2b) Final_Graphs: - Figure_2A.xlsx contains the x-y source file for figure 2A

2c) Analysis: - FCS_Fits.xlsx outcome of the global fitting procedure described in the .docx below (each group of points represents a certain composition and calcium concentration, read the Raw_Data_Description.docx in the FCS > Raw_Data) - Notes_for_FCS_Analysis.docx contains a brief description of the analysis of the autocorrelation curves

3) GPLaurdan 3a) Raw Data: all the spectra are stored in folders named by date (yymmdd_lipidcomposition_Laurdan) and are in both .FS and .txt formats

3b) GP calculations: contains all the .xlsx files calculating the GP values from the raw emission and excitation spectra

3c) Final_Graphs - Data_Processing_For_Fig_2D.csv contains the data processing from the GP values calculated from the spectra to the DeltaGP (GP with- GP without CaM) reported in fig. 2D - Figure_2C_2D.xlsx contains the x-y source file for the figure 2C and 2D

4) LiveCellsImaging

3a) Intensity_Protrusions_vs_Cell_Body: - contains all the .xlsx files calculating the intensity of the various images. File renamed by date (yymmdd) - All data in all excel sheets gathered in another Excel file to create a final graph

3b) Final_Graphs - Figure_S2B.xlsx contains the x-y source file for the figure S2B

5) LiveCellImaging_Raw_Data: it contains some of the images, which are given in .tif. They are divided by date (yymmdd) and each contains subfolders renamed by sample name, concentration of ionomycin. Within the subfolders, the images are divided into folders distinguishing the data acquired before and after the ionomycin treatment and the incubation time.

6) 211124_BioCev_Imaging_1 folder has the .jpg files of the time laps, these are shown in fig 1A and S2.

7) 211124_BioCev_Imaging_2 and 8) 211124_BioCev_Imaging_3 contain the images of HeLa cells expressing EGFP-CaM after treatment with ionomycin 200 nM (A1) and 1 uM (A2), respectively.

9) SPR

9a) Raw Data: - SPR_Raw_Data.xlsx x/y exported sensorgrams - the .jpg files of the software are also reported and named by lipid composition

9b) Final_Graph: - Fig.2B.xlsx contains the x-y source file for the figure 2B

9c) Analysis - SPR_Analysis.xlsx: excel file containing step-by-step (sheet by sheet) how we processed the raw data to obtain the final figure (details explained in the .docx below) - Analysis of SPR data_notes.docx: read me for detailed explanation