Dataset Overview: Contains sales data from Blinkit, including product details, order quantities, revenue, and timestamps. Useful for demand forecasting, price optimization, trend analysis, and business insights. Helps in understanding customer behavior and seasonal variations in online grocery shopping. Potential Use Cases: - Time Series Analysis: Analyze sales trends over different periods. - Demand Forecasting: Predict future product demand based on historical data. - Price Optimization: Identify the impact of pricing on sales and revenue. - Customer Behavior Analysis: Understand buying patterns and preferences. - Market Trends: Explore how different factors affect grocery sales performance. This dataset can be beneficial for data scientists, business analysts, and researchers looking to explore e-commerce and retail trends. Feel free to use it for analysis, machine learning models, and business intelligence projects. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2Fa633fb36dc370263696b5d2ec940c74f%2FScreenshot%202025-06-16%20082824.png?generation=1750086765806732&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2F8843129c88c2f57d66006a3ac9d37dc7%2FScreenshot%202025-06-16%20084001.png?generation=1750086777975125&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16299142%2Ffa4f29a8f4cc763a1cc66c7913c077e8%2FScreenshot%202025-06-16%20084007.png?generation=1750086787100561&alt=media" alt="">

An interactive dashboard to visualize sales performance, product categories, regional performance, and key business KPIs.

📌 Description This project analyzes online sales data using Power BI, focusing on performance metrics such as Total Sales Amount, Profit, Quantity Sold, and Payment Modes. The dashboard provides detailed visualizations to identify top-performing categories, sub-categories, and locations. It aims to deliver actionable insights for business strategy, marketing decisions, and operational improvements.

The dataset is split across two CSV files:

• Orders.csv – contains customer and order metadata (date, name, location)

• Details.csv – contains order-level details (profit, quantity, payment mode, category)

🧩 Key Features - KPI Cards: Total Amount, Total Profit, Total Quantity, Profit Margin

• Pie Charts: Sales by Category, Sales by Payment Mode

• Donut Chart: Sales by State

• Bar Chart: Sales by Sub-Category

• Map: Quantity sold across Indian States

• Interactive Slicers and Filters

⚒️ Tools & Techniques Power BI Desktop

DAX Calculations

Custom Visual Design for Clean UI/UX

This dataset is about books. It has 1 row and is filtered where the book is Pro Power BI Desktop : Free Interactive Data Analysis with Microsoft Power BI. It features 7 columns including author, publication date, language, and book publisher.

Dataset

This dataset was created by AmitRaghav007

Contents

Dataset

This dataset was created by Tanishq Tanna

Contents

This dataset is about book subjects. It has 3 rows and is filtered where the books is Beginning big data with Power BI and Excel 2013 : big data processing and analysis using Power BI in Excel 2013. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Power BI Dashboard : https://www.mavenanalytics.io/project/3776

The IPL (Indian Premier League) is one of the most popular and widely followed cricket leagues in the world. It features top cricket players from around the world playing for various franchise teams in India. The league is known for its high-scoring matches, intense rivalries, and innovative marketing strategies.

If you are a data enthusiast or a cricket fan, you will be excited to know that there is a dataset available on Kaggle that contains comprehensive information about the IPL matches played over the years. This dataset is a valuable resource for anyone interested in analyzing the performance of players and teams in the league.

The IPL dataset on Kaggle contains information on over 800 IPL matches played from 2008 to 2020. It includes details on the date, time, venue, teams, players, and various statistics such as runs scored, wickets taken, and more. The dataset also contains information on the individual performances of players and teams, as well as the overall performance of the league over the years.

The IPL dataset is a goldmine for data analysts and cricket enthusiasts alike. It provides a wealth of information that can be used to uncover insights about the league and its players. For example, you can use the dataset to analyze the performance of a particular player or team over the years, or to identify trends in the league such as changes in team strategies or the emergence of new players.

If you are new to data analysis, the IPL dataset is a great place to start. You can use it to learn how to use tools such as Excel or Power BI to create visualizations and gain insights from data. With the right skills and tools, you can use the IPL dataset to create interactive dashboards and reports that provide valuable insights into the world of cricket.

Overall, the IPL dataset on Kaggle is an excellent resource for anyone interested in cricket or data analysis. It contains a wealth of information that can be used to analyze and gain insights into the performance of players and teams in one of the most exciting cricket leagues in the world.

This dataset contains points table and player Information. To view more data such as Match stats, Ball_by_ball data & Player innings data, Please visit the below links:

Match stats, Ball_by_ball data: https://www.kaggle.com/datasets/biswajitbrahmma/ipl-complete-dataset-2008-2022

Player innings data: https://www.kaggle.com/datasets/paritosh712/cricket-every-single-ipl-inning-20082022

Thanks to Biswajit Brahmma & Paritosh Anand for their dataset.

What is IPGOD?

The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD.

How do I use IPGOD?

IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar.

IP Data Platform

IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform

References

The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset.

• Patents
• Trade Marks
• Designs
• Plant Breeder’s Rights

Updates

Tables and columns

Due to the changes in our systems, some tables have been affected.

• We have added IPGOD 225 and IPGOD 325 to the dataset!
• The IPGOD 206 table is not available this year.
• Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use.

Data quality improvements

Data quality has been improved across all tables.

• Null values are simply empty rather than '31/12/9999'.
• All date columns are now in ISO format 'yyyy-mm-dd'.
• All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0.
• All tables are encoded in UTF-8.
• All tables use the backslash \ as the escape character.
• The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

This Rio Grande and Pecos River Water Operations Dashboard was created using the Microsoft Power BI application and is currently available to the public. This dashboard was created to provide real time data of the Rio Grande and Pecos rivers and reservoirs for water operation managers to assist in monitoring and making decisions. Data includes 15-minute water flow data and reservoir elevation and storage data from the U.S. Geological Survey, Colorado Department of Water Resources, and U.S. Bureau of Reclamation. The water operations dashboard is in an easy to navigate format that allows the user to clearly view current river and reservoir data at a single website to help make operations, management, and planning decisions.

Dataset

This dataset was created by mbaabu Harun Mwenda

Contents

In the beginning, the case was just data for a company that did not indicate any useful information that would help decision-makers. In this case, after collecting a number of revenues and expenses over the months. Needed to know the answers to a number of questions to make important decisions based on intuition-free data. The Questions:- About Rev. & Exp.
- What is the total sales and profit for the whole period? And What Total products sold? And What is Net profit? - In which month was the highest percentage of revenue achieved? And in the same month, what is the largest day have amount of revenue? - In which month was the highest percentage of expenses achieved? And in the same month, what is the largest day have amount of exp.? - What is the extent of the change in expenditures for each month? Percentage change in net profit over the months? About Distribution - What is the number of products sold each month in the largest state? -The top 3 largest states buying products during the two years? Comparison - Between Sales Method by Sales? - Between Men and Women’s Product by Sales? - Between Retailer by Profit?

What I did? - Understanding the data - preprocessing and clean the data - Solve The problems in the cleaning like missing data or false type data - querying the data and make some calculations like "COGS" with power query "Excel". - Modeling and make some measures on the data with power pivot "Excel" - After finishing processing and preparation, I made Some Pivot tables to answers the questions. - Last, I made a dashboard with Power BI to visualize The Results.

This Power BI dashboard shows the COVID-19 vaccination rate by key demographics including age groups, race and ethnicity, and sex for Tempe zip codes.Data Source: Maricopa County GIS Open Data weekly count of COVID-19 vaccinations. The data were reformatted from the source data to accommodate dashboard configuration. The Maricopa County Department of Public Health (MCDPH) releases the COVID-19 vaccination data for each zip code and city in Maricopa County at ~12:00 PM weekly on Wednesdays via the Maricopa County GIS Open Data website (https://data-maricopa.opendata.arcgis.com/). More information about the data is available on the Maricopa County COVID-19 Vaccine Data page (https://www.maricopa.gov/5671/Public-Vaccine-Data#dashboard). The dashboard’s values are refreshed at 3:00 PM weekly on Wednesdays. The most recent date included on the dashboard is available by hovering over the last point on the right-hand side of each chart. Please note that the times when the Maricopa County Department of Public Health (MCDPH) releases weekly data for COVID-19 vaccines may vary. If data are not released by the time of the scheduled dashboard refresh, the values may appear on the dashboard with the next data release, which may be one or more days after the last scheduled release.Dates: Updated data shows publishing dates which represents values from the previous calendar week (Sunday through Saturday). For more details on data reporting, please see the Maricopa County COVID-19 data reporting notes at https://www.maricopa.gov/5460/Coronavirus-Disease-2019.

Dataset

This dataset was created by M0stafa Nasser

Released under MIT

Contents

Dataset

This dataset was created by Muhammad Faheem Naeem

Contents

From 2016 to 2018, we surveyed the world’s largest natural history museum collections to begin mapping this globally distributed scientific infrastructure. The resulting dataset includes 73 institutions across the globe. It has:

• Basic institution data for the 73 contributing institutions, including estimated total collection sizes, geographic locations (to the city) and latitude/longitude, and Research Organization Registry (ROR) identifiers where available.

• Resourcing information, covering the numbers of research, collections and volunteer staff in each institution.

• Indicators of the presence and size of collections within each institution broken down into a grid of 19 collection disciplines and 16 geographic regions.

• Measures of the depth and breadth of individual researcher experience across the same disciplines and geographic regions.

This dataset contains the data (raw and processed) collected for the survey, and specifications for the schema used to store the data. It includes:

1. A diagram of the MySQL database schema.
2. A SQL dump of the MySQL database schema, excluding the data.
3. A SQL dump of the MySQL database schema with all data. This may be imported into an instance of MySQL Server to create a complete reconstruction of the database.
4. Raw data from each database table in CSV format.
5. A set of more human-readable views of the data in CSV format. These correspond to the database tables, but foreign keys are substituted for values from the linked tables to make the data easier to read and analyse.
6. A text file containing the definitions of the size categories used in the collection_unit table.

The global collections data may also be accessed at https://rebrand.ly/global-collections. This is a preliminary dashboard, constructed and published using Microsoft Power BI, that enables the exploration of the data through a set of visualisations and filters. The dashboard consists of three pages:

Institutional profile: Enables the selection of a specific institution and provides summary information on the institution and its location, staffing, total collection size, collection breakdown and researcher expertise.

Overall heatmap: Supports an interactive exploration of the global picture, including a heatmap of collection distribution across the discipline and geographic categories, and visualisations that demonstrate the relative breadth of collections across institutions and correlations between collection size and breadth. Various filters allow the focus to be refined to specific regions and collection sizes.

Browse: Provides some alternative methods of filtering and visualising the global dataset to look at patterns in the distribution and size of different types of collections across the global view.

Debt-To-Capital-Ratio Time Series for Microsoft Corporation. Microsoft Corporation develops and supports software, services, devices, and solutions worldwide. The company's Productivity and Business Processes segment offers Microsoft 365 Commercial, Enterprise Mobility + Security, Windows Commercial, Power BI, Exchange, SharePoint, Microsoft Teams, Security and Compliance, and Copilot; Microsoft 365 Commercial products, such as Windows Commercial on-premises and Office licensed services; Microsoft 365 Consumer products and cloud services, such as Microsoft 365 Consumer subscriptions, Office licensed on-premises, and other consumer services; LinkedIn; Dynamics products and cloud services, such as Dynamics 365, cloud-based applications, and on-premises ERP and CRM applications. Its Intelligent Cloud segment provides Server products and cloud services, such as Azure and other cloud services, GitHub, Nuance Healthcare, virtual desktop offerings, and other cloud services; Server products, including SQL and Windows Server, Visual Studio and System Center related Client Access Licenses, and other on-premises offerings; Enterprise and partner services, including Enterprise Support and Nuance professional Services, Industry Solutions, Microsoft Partner Network, and Learning Experience. The company's Personal Computing segment provides Windows and Devices, such as Windows OEM licensing and Devices and Surface and PC accessories; Gaming services and solutions, such as Xbox hardware, content, and services, first- and third-party content Xbox Game Pass, subscriptions, and Cloud Gaming, advertising, and other cloud services; search and news advertising services, such as Bing and Copilot, Microsoft News and Edge, and third-party affiliates. It sells its products through OEMs, distributors, and resellers; and online and retail stores. The company was founded in 1975 and is headquartered in Redmond, Washington.

Comprehensive dataset containing 1,454 verified Power station businesses in Indonesia with complete contact information, ratings, reviews, and location data.

🌍 Europe B2B Company Dataset | 30M+ Verified Records | Firmographics & API Access Power your sales, marketing, and investment strategies with the most comprehensive global B2B company data—verified, AI-driven, and updated bi-weekly.

The Forager.ai Global Company Dataset delivers 30M+ high-quality firmographic records, covering public and private companies worldwide. Leveraging AI-powered validation and bi-weekly updates, our dataset ensures accuracy, freshness, and depth—making it ideal for sales intelligence, market analysis, and CRM enrichment.

📊 Key Features & Coverage ✅ 30M+ Company Records – The largest, most reliable B2B firmographic dataset available. ✅ Bi-Weekly Updates – Stay ahead with refreshed data every two weeks. ✅ AI-Driven Accuracy – Sophisticated algorithms verify and enrich every record. ✅ Global Coverage – Companies across North America, Europe, APAC, and emerging markets.

📋 Core Data Fields: ✔ Company Name, LinkedIn URL, & Domain ✔ Industries ✔ Job postings, Revenue, Employee Size, Funding Status ✔ Location (HQ + Regional Offices) ✔ Tech Stack & Firmographic Signals ✔ LinkedIn Profile details

🎯 Top Use Cases 🔹 Sales & Lead Generation

Build targeted prospect lists using firmographics (size, industry, revenue).

Enhance lead scoring with technographic insights.

🔹 Market & Competitive Intelligence

Track company growth, expansions, and trends.

Benchmark competitors using real-time private company data.

🔹 Venture Capital & Private Equity

Discover investment opportunities with granular sector-level insights.

Monitor portfolio companies and industry shifts.

🔹 ABM & Marketing Automation

Enrich CRM data for hyper-targeted campaigns.

Power intent data and predictive analytics.

⚡ Delivery & Integration Choose the best method for your workflow:

REST API – Real-time access for developers.

Flat Files (CSV, JSON) – Delivered via S3, Wasabi, Snowflake.

Custom Solutions – Scalable enterprise integrations.

🔒 Data Quality & Compliance 95%+ Field Completeness – Minimize gaps in your analysis.

Ethically Sourced – Compliant with GDPR, CCPA, and global privacy laws.

Transparent Licensing – Clear usage terms for peace of mind.

🚀 Why Forager.ai? ✔ AI-Powered Accuracy – Better data, fewer false leads. ✔ Enterprise-Grade Freshness – Bi-weekly updates keep insights relevant. ✔ Flexible Access – API, bulk files, or custom database solutions. ✔ Dedicated Support – Onboarding and SLA-backed assistance.

Tags: B2B Company Data |LinkedIn Job Postings | Firmographics | Global Business Intelligence | Sales Leads | VC & PE Data | Technographics | CRM Enrichment | API Access | AI-Validated Data

Blockchain data query: Power Badge dataset Comparison

Election Facebook’s Ad Metrics 2024: Trump vs. Harris

A key event of 2024 is the U.S. presidential election. This project focuses on analyzing how Donald Trump and Kamala Harris use advertising to win votes, exploring their strategies, actions, and effectiveness.

Here is the Dataset i have used in the analytic:

File name: trump.zip and harris.zip (Original Data)

The files were downloaded from the Facebook Ad Library. The data focuses on two primary accounts: Trump and Harris, which had the highest number of advertisements and the largest ad spend. These accounts promoted two types of campaigns: presidential campaigns and victory funds. However, I will concentrate solely on the presidential campaigns. Date Range: Based on my research, presidential campaigns typically begin about a year before the election. Therefore, I collected data starting from February 25, 2023, the date Harris announced her candidacy to compete with Trump, up to the current date, December 7, 2024.

File name: Trump-Harris add-id.csv (Processed Data)

This is the main data of the "Election Facebook’s Ad Metrics 2024: Trump vs. Harris"

File name: AD-Tech-Analytic-Project-DashBoard.pbix

Power BI chart imported data from Trump-Harris add-id.csv (Processed Data) and some others

File name: 6state trump data.csv, datamichigan.csv, data nevada.csv

Data that filters from Trump-Harris add-id.csv (Processed Data) have been used in AD-Tech-Analytic-Project-DashBoard.pbix