Discover the booming Business Intelligence (BI) Analysis Tools market! This in-depth report reveals a $25 billion market in 2025, projecting 12% CAGR through 2033. Explore key trends, leading companies (Tableau, Power BI, Qlik), and regional insights. Gain a competitive edge with this comprehensive market analysis.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

The global data visualization market, valued at $9.84 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 10.95% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and complexity of data generated across various industries necessitates effective visualization tools for insightful analysis and decision-making. Furthermore, the rising adoption of cloud-based solutions offers scalability, accessibility, and cost-effectiveness, driving market growth. Advances in artificial intelligence (AI) and machine learning (ML) are integrating seamlessly with data visualization platforms, enhancing automation and predictive capabilities, further stimulating market demand. The BFSI (Banking, Financial Services, and Insurance) sector, along with IT and Telecommunications, are major adopters, leveraging data visualization for risk management, fraud detection, customer relationship management, and network optimization. However, challenges remain, including the need for skilled professionals to effectively utilize these tools and concerns regarding data security and privacy. The market segmentation reveals a strong presence of executive management and marketing departments across organizations, highlighting the strategic importance of data visualization in business operations. The market's competitive landscape is characterized by established players like SAS Institute, IBM, Microsoft, and Salesforce (Tableau), along with emerging innovative companies. This competition fosters innovation and drives down costs, making data visualization solutions more accessible to a broader range of businesses and organizations. Regional variations in market penetration are expected, with North America and Europe currently holding significant shares, but Asia Pacific is poised for substantial growth, driven by rapid digitalization and technological advancements in the region. The on-premise deployment mode still holds a considerable market share, though the cloud/on-demand segment is experiencing faster growth due to its inherent advantages. The ongoing trend towards self-service business intelligence (BI) tools is empowering end-users to access and analyze data independently, increasing the overall market demand for user-friendly and intuitive data visualization platforms. Future growth will depend on continued technological advancements, expanding applications across diverse industries, and addressing the existing challenges related to data skills gaps and security concerns. This report provides a comprehensive analysis of the Data Visualization Market, projecting robust growth from $XX Billion in 2025 to $YY Billion by 2033. It covers the period from 2019 to 2033, with a focus on the forecast period 2025-2033 and a base year of 2025. This in-depth study examines key market segments, competitive landscapes, and emerging trends influencing this rapidly evolving industry. The report is designed for executives, investors, and market analysts seeking actionable insights into the future of data visualization. Recent developments include: September 2022: KPI 360, an AI-driven solution that uses real-time data monitoring and prediction to assist manufacturing organizations in seeing various operational data sources through a single, comprehensive industrial intelligence dashboard that sets up in hours, was recently unveiled by SymphonyAI Industrial., January 2022: The most recent version of the IVAAP platform for ubiquitous subsurface visualization and analytics applications was released by INT, a top supplier of data visualization software. IVAAP allows exploring, visualizing, and computing energy data by providing full OSDU Data Platform compatibility. With the new edition, IVAAP's map-based search, data discovery, and data selection are expanded to include 3D seismic volume intersection, 2D seismic overlays, reservoir, and base map widgets for cloud-based visualization of all forms of energy data.. Key drivers for this market are: Cloud Deployment of Data Visualization Solutions, Increasing Need for Quick Decision Making. Potential restraints include: Lack of Tech Savvy and Skilled Workforce/Inability. Notable trends are: Retail Segment to Witness Significant Growth.

Developed a multi-page Power BI dashboard integrating data from multiple sources (Wikipedia, CSV, Excel) to analyze and visualize U.S. states’ population and homicide statistics.

The HR analytics tools market is experiencing robust growth, driven by the increasing need for data-driven decision-making in human resource management. The market, estimated at $15 billion in 2025, is projected to achieve a compound annual growth rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. Firstly, organizations are increasingly leveraging data to optimize recruitment processes, improve employee engagement, and enhance workforce planning. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are enabling more sophisticated analytics capabilities, providing actionable insights into employee behavior, performance, and attrition. Thirdly, the rising adoption of cloud-based HR solutions is facilitating easier access to data and enhanced collaboration across HR teams. The market is segmented by various tools, including Python, RStudio, Tableau, KNIME, Power BI, Microsoft Excel, Orange, and Apache Hadoop, each catering to different analytical needs and organizational scale. Despite the significant growth potential, the market faces certain challenges. Data privacy and security concerns remain a major hurdle, especially given the sensitive nature of employee data. The lack of skilled professionals proficient in data analytics and HR practices also presents a limitation. Furthermore, the integration of disparate HR data sources can be complex and time-consuming. However, these challenges are being addressed through the development of robust data security protocols, specialized training programs, and integrated HR software solutions. The North American region currently holds the largest market share, but Asia-Pacific is anticipated to show the fastest growth in the coming years due to the increasing adoption of HR analytics tools in rapidly growing economies.

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

The global Healthcare Business Intelligence (BI) Software market is experiencing robust growth, driven by the increasing need for data-driven decision-making within healthcare organizations. The market's expansion is fueled by several key factors: the rising adoption of electronic health records (EHRs) generating massive datasets, the imperative for improved operational efficiency and cost reduction, the demand for personalized medicine and proactive patient care, and the growing regulatory compliance requirements demanding advanced analytics capabilities. The market is segmented by deployment (cloud-based and on-premise), by component (software, services), and by end-user (hospitals, clinics, pharmaceutical companies, etc.). While precise market sizing data isn't provided, based on industry reports and the listed prominent players (Domo, Sisense, Tableau, Microsoft, etc.), a reasonable estimate for the 2025 market size could be around $15 billion, with a projected Compound Annual Growth Rate (CAGR) of 15-20% through 2033. This growth trajectory reflects the continuous investment in healthcare IT infrastructure and the increasing sophistication of BI tools tailored for the complexities of healthcare data. Several trends are shaping the future of this market. These include the growing adoption of Artificial Intelligence (AI) and Machine Learning (ML) within BI platforms to provide predictive analytics and improved insights, the rise of cloud-based BI solutions offering scalability and cost-effectiveness, the increasing demand for real-time data visualization and dashboards for immediate decision support, and the focus on data security and privacy compliance within the healthcare sector. While the market faces restraints such as high initial investment costs, the complexity of integrating data from diverse sources, and the need for skilled professionals to manage and interpret the data, the significant benefits offered by BI solutions are driving widespread adoption and making these challenges manageable. The competitive landscape is highly dynamic, with established players like Microsoft and Tableau alongside specialized healthcare BI providers like DashboardMD and CareVoyant, leading to innovation and improved solutions for diverse healthcare needs.

Explore the booming Data Visualization market! Discover key insights, growth drivers, market size estimations, and CAGR trends for 2025-2033. Understand applications, types, and leading companies in this essential business intelligence sector.

The Data Lens (Visualizations of Data) market is experiencing robust growth, driven by the increasing need for businesses to derive actionable insights from complex datasets. The market, currently estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors, including the rising adoption of cloud-based analytics platforms, the proliferation of big data, and the growing demand for data-driven decision-making across diverse industries. Businesses are increasingly recognizing the importance of visualizing data to identify trends, patterns, and anomalies, leading to improved operational efficiency, enhanced strategic planning, and better customer understanding. The market is segmented by various software solutions, including business intelligence platforms (like Tableau, Sisense, and Qlikview), data visualization tools (such as Plotly and Chartio), and specialized analytics platforms from vendors like Alteryx and IBM. The competitive landscape is dynamic, with established players and innovative startups vying for market share through continuous product development and strategic partnerships. The continued expansion of the Data Lens market is expected to be further propelled by advancements in artificial intelligence (AI) and machine learning (ML), which are enhancing the capabilities of data visualization tools. AI-powered features such as automated insights generation and predictive analytics are transforming how businesses interact with and interpret their data. Geographic expansion, particularly in emerging economies, is another significant growth driver. However, challenges remain, including the need for skilled data analysts to effectively utilize these tools and the complexity associated with integrating diverse data sources. Nevertheless, the overall outlook for the Data Lens market remains highly positive, indicating a sustained period of substantial growth and innovation throughout the forecast period.

The Data Virtualization Cloud market is experiencing steady growth, projected to reach $1929.1 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 2.3%. This growth is fueled by several key factors. The increasing need for real-time data access and integration across diverse data sources is driving demand. Organizations are seeking efficient and cost-effective solutions to manage and analyze their exponentially growing data volumes, leading to the adoption of cloud-based data virtualization platforms. The rise of hybrid cloud environments further accelerates this trend, as businesses need seamless data access across on-premises and cloud-based systems. Furthermore, the increasing adoption of advanced analytics and business intelligence tools necessitates robust data virtualization capabilities to provide a unified and consistent view of data. Competitive pressures also push businesses to improve operational efficiency and agility, with data virtualization playing a crucial role in achieving these goals. Major market players like Denodo, Microsoft, Google, Alibaba, IBM, Informatica, Oracle, SAP, Tibco, Datometry, and VMware are actively contributing to market expansion through continuous innovation and strategic partnerships. However, challenges remain, including complexities in data governance and security, the need for skilled professionals to implement and manage these solutions, and potential integration challenges with legacy systems. Despite these obstacles, the long-term outlook for the Data Virtualization Cloud market remains positive, driven by ongoing technological advancements and increasing enterprise adoption. The market is expected to experience consistent growth throughout the forecast period (2025-2033), driven by factors such as the increasing volume and variety of data, the need for improved data accessibility, and the rise of cloud computing.

MedSynora DW – A Comprehensive Synthetic Hospital Patient Data Warehouse

Overview MedSynora DW is a huge synthetic dataset designed to simulate the operation flow by adopting a patient-based approach in a large hospital. This dataset covers patient encounters, treatments, lab tests, vital signs, cost details and more over a full year of 2024. It is developed to support data science, machine learning, and business intelligence projects in the healthcare domain.

Project Highlights • Realistic Simulation: Generated using advanced Python scripts and statistical models, the dataset reflects realistic hospital operations and patient flows without using any real patient data. • Comprehensive Schema: The data warehouse includes multiple fact and dimension tables: o Fact Tables: Encounter, Treatment, Lab Tests, Special Tests, Vitals, and Cost. o Dimension Tables: Patient, Doctor, Disease, Insurance, Room, Date, Chronic Diseases, Allergies, and Additional Services. o Bridge Tables: For managing many-to-many relationships (e.g., doctors per encounter) and some other… • Synthetic & Scalable: The dataset is entirely synthetic, ensuring privacy and compliance. It is designed to be scalable – the current version simulates around 145,000 encounter records.

Data Generation • Data Sources & Methods: Data is generated using bunch of Py libraries. Highly customized algorithms simulate realistic patient demographics, doctor assignments, treatment choices, lab test results, and cost breakdowns etc.. • Diverse Scenarios: With over 300 diseases and thousands of treatment variations, along with dozens of lab and special tests, the dataset offers profoundly rich variability to support complex analytical projects.

How to Use This Dataset • For Data Modeling & ETL Testing: Import the CSV files into your favorite database system (e.g., PostgreSQL, MySQL, or directly into a BI tool like Power BI) and set up relationships as described in the accompanying documentation. • For Machine Learning Projects: Use the dataset to build predictive models related to patient outcomes, cost analysis, or treatment efficacy. • For Educational Purposes: Ideal for learning about data warehousing, star schema design, and advanced analytics in healthcare.

Final Note MedSynora DW offers a unique opportunity to experiment with a comprehensive, realistic hospital data warehouse without compromising real patient information. Enjoy exploring, analyzing, and building with this dataset – and feel free to reach out if you have any questions or suggestions. In particular, inconsistencies, deficiencies or suggestions about the dataset by experts in the field will contribute to other version improvements.

According to Cognitive Market Research, the global Business Intelligence market size is USD 16.9 million in 2023 and will expand at a compound annual growth rate (CAGR) of 9.50% from 2023 to 2030.

The demand for Business Intelligence s is rising due to the increasing data complexity and rising focus on data-driven decision-making.
Demand for adults remains higher in the Business Intelligence market.
The Business intelligence platform category held the highest Business intelligence market revenue share in 2023.
North American Business Intelligence will continue to lead, whereas the Asia-Pacific Business Intelligence market will experience the most substantial growth until 2030.

Growing Emphasis on Data-Driven Decision-Making to Provide Viable Market Output

In the Business Intelligence Tools market, the increasing recognition of the strategic importance of data-driven decision-making serves as a primary driver. Organizations across various industries are realizing the transformative power of insights derived from BI tools. As the volume of data generated continues to soar, businesses seek sophisticated tools that can efficiently analyze and interpret this information. The ability of BI tools to convert raw data into actionable insights empowers decision-makers to formulate informed strategies, enhance operational efficiency, and gain a competitive edge in a data-centric business landscape.

In June 2020, SAS and Microsoft established a comprehensive technology and go-to-market strategic alliance. As part of the collaboration, SAS's industry solutions and analytical products will be moved to Microsoft Azure, SAS Cloud's preferred cloud provider.

Source-news.microsoft.com/2020/06/15/sas-and-microsoft-partner-to-further-shape-the-future-of-analytics-and-ai/#:~:text=and%20SAS%20today%20announced%20an,from%20their%20digital%20transformation%20initiatives.

Rise in Adoption of Advanced Analytics and Artificial Intelligence to Propel Market Growth

Another significant driver in the Business Intelligence Tools market is the escalating adoption of advanced analytics and artificial intelligence (AI) capabilities. Modern BI tools are incorporating AI-driven functionalities such as machine learning algorithms, natural language processing, and predictive analytics. These technologies enable users to uncover deeper insights, identify patterns, and predict future trends. The integration of AI not only enhances the analytical capabilities of BI tools but also automates processes, reducing manual efforts and improving the overall efficiency of data analysis. This trend aligns with the industry's pursuit of more intelligent and automated BI solutions to derive maximum value from data assets.

In March 2020, IBM created a new, dynamic global dashboard to display the global spread of COVID-19 with the assistance of IBM Cognos Analytics. The World Health Organization (WHO) and state and municipal governments provide the COVID-19 data displayed in this dashboard.

Source-www.ibm.com/blog/creating-trusted-covid-19-data-for-communities/

Market Dynamics of the Business Intelligence tool Market

Key Drivers for Business Intelligence tool Market

Increasing Demand for Data-Driven Decision Making Across Various Sectors: As companies produce vast amounts of data, there is an escalating requirement for tools that can analyze and convert raw data into actionable insights. Business Intelligence (BI) tools facilitate quicker and more precise strategic decisions in areas such as sales, finance, operations, and customer service.

Transition to Cloud-Based BI Solutions for Enhanced Scalability and Accessibility: Organizations are progressively shifting from on-premise BI systems to cloud-based solutions, which provide real-time access, foster collaboration, and reduce infrastructure expenses. This transition enhances scalability and accommodates hybrid or remote work settings.

Incorporation of AI and Machine Learning for Enhanced Predictive Analytics: Sophisticated BI tools are incorporating artificial intelligence and machine learning technologies to deliver predictive forecasting, anomaly detection, and natural language querying—thereby improving the accuracy of business forecasts and enhancing user accessibility.

Key Restraints for Business Intelligence tool Market

High Initial Setup and Customization Costs for SMEs: Small and medium-sized...

Context

This dataset was curated from the Bing search logs (desktop users only) over the period of Jan 1st, 2020 – (Current Month - 1). Only searches that were issued many times by multiple users were included. Dataset includes queries from all over the world that had an intent related to the Coronavirus or Covid-19. In some cases this intent is explicit in the query itself, e.g. “Coronavirus updates Seattle” in other cases it is implicit , e.g. “Shelter in place”. Implicit intent of search queries (e.g. Toilet paper) were extracted by using Random walks on the click graph approach as outlined in the following paper by Nick Craswell et al at Microsoft Research: https://www.microsoft.com/en-us/research/wp-content/uploads/2007/07/craswellszummer-random-walks-sigir07.pdf All personal data was removed. Source - https://msropendata.com/datasets/c5031874-835c-48ed-8b6d-31de2dad0654

Acknowledgements

Data Source: Bing Coronavirus Query set (https://github.com/microsoft/BingCoronavirusQuerySet)

License - Open Use of Data Agreement v1.0

Content

Inside the data folder there is a folder 2020 (for the year) which contains two kinds of files.

QueriesByCountry_DateRange.tsv : A tab separated text file that contains queries with Coronavirus intent by Country. QueriesByState_DateRange.tsv : A tab separated text file that contains queries with Coronavirus intent by State.

QueriesByCountry Date : string, Date on which the query was issued.

Query : string, The actual search query issued by user(s).

IsImplicitIntent : bool, True if query did not mention covid or coronavirus or sarsncov2 (e.g, “Shelter in place”). False otherwise.

Country : string, Country from where the query was issued.

PopularityScore : int, Value between 1 and 100 inclusive. 1 indicates least popular query on the day/Country with Coronavirus intent, and 100 indicates the most popular query for the same Country on the same day.

QueriesByState Date : string, Date on which the query was issued.

Query : string, The actual search query issued by user(s).

IsImplicitIntent : bool, True if query did not mention covid or coronavirus or sarsncov2 (e.g, “Shelter in place”). False otherwise.

State : string, State from where the query was issued.

Country :string, Country from where the query was issued.

PopularityScore : int, Value between 1 and 100 inclusive. 1 indicates least popular query on the day/State/Country with Coronavirus intent, and 100 indicates the most popular query for the same geogrpahy on the same day.

🛫 Airplane Crash Data (1919–2025) – Cleaned & Unified 📌 Overview This dataset is a comprehensive and manually curated collection of global aviation accidents and incidents from 1919 to 2025, sourced from five authoritative platforms. It combines historical and modern records into a single, clean, and analysis-ready .csv file — ideal for data science, machine learning, and aviation safety research.

📂 Sources Used The raw data was gathered from the following sources:

• https://asn.flightsafety.org/database/
• https://www.kaggle.com/datasets/saurograndi/airplane-crashes-since-1908
• https://www.panish.law/aviation_accident_statistics.html
• https://en.wikipedia.org/wiki/Aviation_accidents_and_incidents
• + 2 more manually scraped sources

Each source had unique attributes, structures, and formats. I manually extracted, cleaned, de-duplicated, and unified the datasets to generate this high-quality final version.

🧹 Data Cleaning & Curation The dataset preparation involved:

🧭 Date standardization across multiple formats (including parsing old historical dates)

🔍 Duplicate removal from overlapping sources

🛬 Location normalization (city, country, coordinates where possible)

📉 Fatality/injury counts harmonized into consistent columns

🧑‍✈️ Flight purpose categorization (commercial, military, training, etc.)

💥 Cause/description refinement to improve textual analysis usability

🏷️ Tagging & classification based on incident severity, aircraft type, etc.

📊 Columns in cleaned_data.csv(this is combination of all databased ,ready to work on) Below is a typical structure of the dataset:

Column Name Description Date :Date of the incident Location :City/Region/Country of the crash Operator :Airline or aircraft operator Flight No :Flight number (if available) Aircraft Type :Type/model of the aircraft Registration :Aircraft registration number Fatalities :Total number of fatalities Aboard :Total number of people on board Ground Fatalities :Number of people killed on the ground (if any) Summary :Short description or probable cause Source :Original source from which the data point was collected Crash Type :Categorized tag: e.g., Mid-air collision, engine failure, pilot error, etc. Year :Extracted year (useful for trend analysis)

Note: Not all columns are present in each original file; where possible, missing data has been filled or marked appropriately.

🔍 Why This Dataset Is Unique 📅 Over a century of aviation data (1919–2025)

🔄 Merged from five reputable sources

🧼 Thorough manual cleaning and validation

📚 Useful for:

Aviation safety analysis

Time-series forecasting

Natural Language Processing (NLP) on crash summaries

Machine learning (e.g., predicting crash causes or fatalities)

📌 Suggested Use Cases ✈️ Predictive modeling of aviation risk

📉 Trend analysis in global air safety

🗺️ Geographic visualization of accident hotspots

🤖 NLP classification of crash summaries

📊 Dashboard creation in Power BI or Tableau

📁 File Included cleaned_data.csv – Final cleaned dataset with unified schema

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.

This project presents a comprehensive analysis of global electricity production by various sources—coal, gas, nuclear, hydro, oil, solar, wind, bioenergy, and other renewables—across different countries and regions. The dataset, compiled from reliable international energy sources, has been cleaned and structured to support multi-platform exploration.

The analysis is carried out using Python (for data preprocessing and visualization), Microsoft Excel (for pivot tables and charts), Tableau (for interactive dashboards), and Power BI (for dynamic reporting). Each tool complements the others by offering diverse perspectives on electricity production patterns. From geographic visualizations to trend analysis, this multi-tool project highlights energy source dominance, regional disparities, and the pace of renewable adoption worldwide—contributing to informed discussions on energy transition and sustainability.

Columns description:

The dataset contains country-wise electricity production data categorized by energy source. The ‘Country’ column lists the country or region (e.g., ASEAN, G20, OECD), while the ‘Code’ column includes country codes (though often left blank). The ‘Year’ column specifies the year of each data entry. Energy production is measured in terawatt-hours (TWh) across multiple sources: coal, gas, and oil represent fossil fuels; nuclear captures electricity from atomic energy; and hydro, wind, solar, and bioenergy represent renewables.

An additional column, ‘Other renewables excluding bioenergy’, covers sources like geothermal and less common renewables. Together, these columns provide a comprehensive overview of each country's electricity production profile across different technologies and timelines.

About This File

The dataset titled "pokemon_dataset.csv" is a cleaned and consolidated version of a relational database initially created for visualizing Pokémon stats across typings and generations in Power BI. This dataset provides a single, well-structured table containing comprehensive Pokémon information, designed for ease of use in various data tools and platforms.

Objective

The primary goal of this dataset is to enable Pokémon fans, data analysts, and visualization enthusiasts to:
- Explore Pokémon stats across different generations and types.
- Build analytical projects and dashboards.
- Gain rich insights into the Pokémon universe.

Data Source

The data was sourced from PokéAPI, an open and accessible RESTful API for Pokémon-related data.
- PokéAPI provides detailed information about Pokémon species, moves, abilities, stats, and more, making it a trusted resource for Pokémon datasets.

Data Cleaning and Preparation

Standardization:
- Datasets from PokéAPI were standardized using a common pokemon_id to ensure consistency and compatibility.

Access Methodology:
- Data was accessed using Python's requests library to fetch JSON objects from PokéAPI.
- These objects were flattened, passed into SQL for relational mapping, and cleaned to produce the final dataset.

Optimization:
- The dataset is optimized for use in tools such as:
- Excel
- Google Sheets
- SQL
- pandas
- Power BI

File Structure

The dataset consists of the following columns:

Target Audience

This dataset is tailored for:
- Pokémon fans who love exploring data and gaining deeper insights into their favorite Pokémon.
- Data analysts and developers creating Pokémon-related projects.

Expected Outcomes

• Discover insights: Learn interesting tidbits about your favorite Pokémon.
• Data-backed discussions: Engage in meaningful comparisons among Pokémon fans.
• Create tools and visualizations: Showcase Pokémon stats and trends in creative ways.

Limitations

• Regional Forms: This dataset does not include regional forms, as their stats are identical to their original counterparts.

Uniquely Popular Businesses

Rankings of Business Categories in Seattle & NYC Neighborhoods

By data.world's Admin [source]

About this dataset

This dataset contains data used to analyze the uniquely popular business types in the neighborhoods of Seattle and New York City. We used publically available neighborhood-level shapefiles to identify neighborhoods, and then crossed that information against Yelp's Business Category API to find businesses operating within each neighborhood. The ratio of businesses from each category was studied in comparison to their ratios in the entire city to determine any significant differences between each borough.

Any single business with more than one category was repeated for each one, however none of them were ever recorded twice for any single category. Moreover, if a certain business type didn't make up at least 1% of a particular neighborhood's businesses overall it was removed from the analysis altogether.

The data available here is free to use under MIT license, with appropriate attribution given back to Yelp for providing this information. It is an invaluable resource for researchers across different disciplines looking into consumer behavior or clustering within urban areas!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

How to Use This Dataset

To get started using this dataset: - Download the appropriate file for the area you’re researching - either salt5_Seattle.csv or top5_NewYorkCity.csv - from the Kaggle site which hosts this dataset (https://www.kaggle.com/puddingmagazine/uniquely-popular-businesses). - Read through each columns information available under Columns section associated with this kaggle description (above).
- Take note of columns that are relevant to your analysis such as nCount which indicates the number of businesses in a neighborhood, rank which shows how popular that business type is overall and neighborhoodTotal which specifies total number of businesses in a particular neighborhood etc.,
- ) Load your selected file into an application designed for data analysis such as Jupyter Notebook, Microsoft Excel, Power BI etc.,
- ) Begin performing various analyses related to understanding where certain types of unique business are most common by subsetting rows based on specific neighborhoods; alternatively perform regressions-based analyses related to trends similar unique type's ranks over multiple neighborhoods etc.,

If you have any questions about interpreting data from this source please reach out if needed!

Research Ideas

• Analyzing the unique business trends in Seattle and New York City to identify potential investment opportunities.
• Creating a tool that helps businesses understand what local competitions they face by neighborhood.
• Exploring the distinctions between neighborhoods by plotting out the different businesses they have in comparison with each other and other cities

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: top5_Seattle.csv | Column name | Description | |:----------------------|:----------------------------------------------------------------------------------------------------------------------------------| | neighborhood | Name of the neighborhood. (String) | | yelpAlias | The Yelp-specified Alias for the business type. (String) | | yelpTitle | The Title given to this business type by Yelp. (String) | | nCount | Number of businesses with this type within a particular neighborhood. (Integer) | | neighborhoodTotal | Total number of businesses located within that particular region. (Integer) | | cCount | Number of businesses with this storefront within an entire city. (Integer) | | cityTotal | Total number of all types of storefronts within an entire city. (Integer) ...

*****Documentation Process***** 1. Data Preparation: - Upload the data into Power Query to assess quality and identify duplicate values, if any. - Verify data quality and types for each column, addressing any miswriting or inconsistencies. 2. Data Management: - Duplicate the original data sheet for future reference and label the new sheet as the "Working File" to preserve the integrity of the original dataset. 3. Understanding Metrics: - Clarify the meaning of column headers, particularly distinguishing between Impressions and Reach, and comprehend how Engagement Rate is calculated. - Engagement Rate formula: Total likes, comments, and shares divided by Reach. 4. Data Integrity Assurance: - Recognize that Impressions should outnumber Reach, reflecting total views versus unique audience size. - Investigate discrepancies between Reach and Impressions to ensure data integrity, identifying and resolving root causes for accurate reporting and analysis. 5. Data Correction: - Collaborate with the relevant team to rectify data inaccuracies, specifically addressing the discrepancy between Impressions and Reach. - Engage with the concerned team to understand the root cause of discrepancies between Impressions and Reach. - Identify instances where Impressions surpass Reach, potentially attributable to data transformation errors. - Following the rectification process, meticulously adjust the dataset to reflect the corrected Impressions and Reach values accurately. - Ensure diligent implementation of the corrections to maintain the integrity and reliability of the data. - Conduct a thorough recalculation of the Engagement Rate post-correction, adhering to rigorous data integrity standards to uphold the credibility of the analysis. 6. Data Enhancement: - Categorize Audience Age into three groups: "Senior Adults" (45+ years), "Mature Adults" (31-45 years), and "Adolescent Adults" (<30 years) within a new column named "Age Group." - Split date and time into separate columns using the text-to-columns option for improved analysis. 7. Temporal Analysis: - Introduce a new column for "Weekend and Weekday," renamed as "Weekday Type," to discern patterns and trends in engagement. - Define time periods by categorizing into "Morning," "Afternoon," "Evening," and "Night" based on time intervals. 8. Sentiment Analysis: - Populate blank cells in the Sentiment column with "Mixed Sentiment," denoting content containing both positive and negative sentiments or ambiguity. 9. Geographical Analysis: - Group countries and obtain additional continent data from an online source (e.g., https://statisticstimes.com/geography/countries-by-continents.php). - Add a new column for "Audience Continent" and utilize XLOOKUP function to retrieve corresponding continent data.

*****Drawing Conclusions and Providing a Summary*****

• The data is equally distributed across different categories, platforms, and over the years.
• Most of our audience comprises senior adults (aged 45 and above).
• Most of our audience exhibit mixed sentiments about our posts. However, an equal portion expresses consistent sentiments.
• The majority of our posts were located in Africa.
• The number of posts increased from the first year to the second year and remained relatively consistent for the third year.
• The optimal time for posting is during the night on weekdays.
• The highest engagement rates were observed in Croatia then Malawi.
• The number of posts targeting senior adults is significantly higher than the other two categories. However, the engagement rates for mature and adolescent adults are also noteworthy, based on the number of targeted posts.

📊 Delhi Metro Ridership & Operational Statistics Dataset

Subtitle

A comprehensive dataset representing ridership, ticket revenue, and operational performance of the Delhi Metro one of the largest urban transit systems in the world.

About the Dataset

The Delhi Metro is a rapid transit system serving the National Capital Region (NCR) of India. It plays a crucial role in reducing traffic congestion and providing sustainable public transportation to millions of passengers every day.

This dataset captures multiple performance indicators of the Delhi Metro network over time, including:

Total metro trips operated Daily total passengers Ticket revenue Average passenger distance traveled per trip Top stations based on passenger demand Total stations operational

These data points help in analyzing metro usage patterns, operational efficiency, and transit demand in the region.

Why This Dataset is Useful

This dataset enables research in:

Urban transport planning Revenue & demand forecasting Passenger travel behavior analysis Transportation infrastructure optimization Dashboard development & data storytelling Academic machine learning projects

Potential Use Cases

• Time-series forecasting of passengers and revenue
• Peak-hour identification using station-based data
• Policy evaluation on fare changes and new lines
• Visualization dashboards (e.g., Plotly/Dash, Power BI, Tableau)

Source

Data has been collected, cleaned, and aggregated using publicly available metro operational insights, news reports, and transit performance summaries released by the Delhi Metro Rail Corporation (DMRC).

File Details

(Adjust fields based on your actual dataset columns — I can refine if you share final structure.)

Licensing

License: CC BY 4.0 (Users must provide attribution when using the dataset)

If you want, I can also add:

Thumbnail Image for Kaggle Dataset Tags & Categories for better discoverability Example Notebooks (Exploration + Forecast models) Dashboard Preview Screenshots