2 datasets found

  1. Global Online Orders

    • kaggle.com
    Updated Oct 8, 2023
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    Javier Sánchez P. (2023). Global Online Orders [Dataset]. https://www.kaggle.com/datasets/javierspdatabase/global-online-orders
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 8, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Javier Sánchez P.
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    Dataset Overview

    Dataset Name: "Nuestro Amazon" E-Commerce Dataset

    General Description: This dataset represents an e-commerce database containing information about products, categories, customers, orders, and more. The data is structured to facilitate analysis and insights into various aspects of an e-commerce business.

    Structure and Attributes: The dataset consists of eight tables: categories, customers, employees, orders, ordersdetails, products, shippers, and suppliers. These tables encompass key information such as product details, customer information, order details.

    Data Source: The data was generated for educational and demonstration purposes to simulate an e-commerce environment. It is not sourced from a real-world e-commerce platform.

    Usage and Applications: This dataset can be utilized for various purposes, including market basket analysis, customer segmentation, sales trends analysis, and supply chain optimization. Analysts and data scientists can derive valuable insights to improve business strategies.

    Acknowledgments and References: The dataset was created for educational use. No specific external sources were referenced for this dataset.

    Explore Interactive Visualizations

    "Quantity per country" in this Kaggle notebook or on Tableau.

    "Orders by country" in this Kaggle notebook or on Tableau.

    Data Analysis

    "Data Analysis of Online Orders" in this Kaggle notebook

    "Data Visualization and Analysis in R" in this Kaggle notebook

  2. w

    Global Data Mining Tool Market Research Report: By Deployment Mode...

    • wiseguyreports.com
    Updated Jan 3, 2025
    + more versions
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    wWiseguy Research Consultants Pvt Ltd (2025). Global Data Mining Tool Market Research Report: By Deployment Mode (On-Premises, Cloud-Based, Hybrid), By Application (Fraud Detection, Customer Segmentation, Market Basket Analysis, Risk Management, Predictive Maintenance), By End User (BFSI, Healthcare, Retail, Telecommunications, Manufacturing), By Data Type (Structured Data, Unstructured Data, Semi-structured Data) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/de/reports/data-mining-tool-market
    Explore at:
    Dataset updated
    Jan 3, 2025
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 20238.36(USD Billion)
    MARKET SIZE 20249.25(USD Billion)
    MARKET SIZE 203220.74(USD Billion)
    SEGMENTS COVEREDDeployment Mode, Application, End User, Data Type, Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICSGrowing demand for big data analytics, Increasing adoption of AI technologies, Rising importance of customer insights, Expanding applications across industries, Enhanced data privacy regulations
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDSAS Institute, Domo, RapidMiner, Microsoft, IBM, DataRobot, TIBCO Software, Oracle, H2O.ai, Sisense, Alteryx, SAP, Tableau, Qlik, Teradata
    MARKET FORECAST PERIOD2025 - 2032
    KEY MARKET OPPORTUNITIESIncreased demand for data analytics, Growth in AI and machine learning, Rising need for big data processing, Cloud-based data mining solutions, Expanding applications across industries
    COMPOUND ANNUAL GROWTH RATE (CAGR) 10.63% (2025 - 2032)

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Javier Sánchez P. (2023). Global Online Orders [Dataset]. https://www.kaggle.com/datasets/javierspdatabase/global-online-orders
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Global Online Orders

Business Data Analysis: Exploration, Visualization, and Insights

Explore at:
3 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Oct 8, 2023
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Javier Sánchez P.
License

Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
License information was derived automatically

Description

Dataset Overview

Dataset Name: "Nuestro Amazon" E-Commerce Dataset

General Description: This dataset represents an e-commerce database containing information about products, categories, customers, orders, and more. The data is structured to facilitate analysis and insights into various aspects of an e-commerce business.

Structure and Attributes: The dataset consists of eight tables: categories, customers, employees, orders, ordersdetails, products, shippers, and suppliers. These tables encompass key information such as product details, customer information, order details.

Data Source: The data was generated for educational and demonstration purposes to simulate an e-commerce environment. It is not sourced from a real-world e-commerce platform.

Usage and Applications: This dataset can be utilized for various purposes, including market basket analysis, customer segmentation, sales trends analysis, and supply chain optimization. Analysts and data scientists can derive valuable insights to improve business strategies.

Acknowledgments and References: The dataset was created for educational use. No specific external sources were referenced for this dataset.

Explore Interactive Visualizations

"Quantity per country" in this Kaggle notebook or on Tableau.

"Orders by country" in this Kaggle notebook or on Tableau.

Data Analysis

"Data Analysis of Online Orders" in this Kaggle notebook

"Data Visualization and Analysis in R" in this Kaggle notebook

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