This Kaggle dataset comes from an output dataset that powers my March Madness Data Analysis dashboard in Domo. - Click here to view this dashboard: Dashboard Link - Click here to view this dashboard features in a Domo blog post: Hoops, Data, and Madness: Unveiling the Ultimate NCAA Dashboard

This dataset offers one the most robust resource you will find to discover key insights through data science and data analytics using historical NCAA Division 1 men's basketball data. This data, sourced from KenPom, goes as far back as 2002 and is updated with the latest 2026 data. This dataset is meticulously structured to provide every piece of information that I could pull from this site as an open-source tool for analysis for March Madness.

Key features of the dataset include: - Historical Data: Provides all historical KenPom data from 2002 to 2026 from the Efficiency, Four Factors (Offense & Defense), Point Distribution, Height/Experience, and Misc. Team Stats endpoints from KenPom's website. Please note that the Height/Experience data only goes as far back as 2007, but every other source contains data from 2002 onward. - Data Granularity: This dataset features an individual line item for every NCAA Division 1 men's basketball team in every season that contains every KenPom metric that you can possibly think of. This dataset has the ability to serve as a single source of truth for your March Madness analysis and provide you with the granularity necessary to perform any type of analysis you can think of. - 2026 Tournament Insights: Contains all seed and region information for the 2026 NCAA March Madness tournament. Please note that I will continually update this dataset with the seed and region information for previous tournaments as I continue to work on this dataset.

These datasets were created by downloading the raw CSV files for each season for the various sections on KenPom's website (Efficiency, Offense, Defense, Point Distribution, Summary, Miscellaneous Team Stats, and Height). All of these raw files were uploaded to Domo and imported into a dataflow using Domo's Magic ETL. In these dataflows, all of the column headers for each of the previous seasons are standardized to the current 2026 naming structure so all of the historical data can be viewed under the exact same field names. All of these cleaned datasets are then appended together, and some additional clean up takes place before ultimately creating the intermediate (INT) datasets that are uploaded to this Kaggle dataset. Once all of the INT datasets were created, I joined all of the tables together on the team name and season so all of these different metrics can be viewed under one single view. From there, I joined an NCAAM Conference & ESPN Team Name Mapping table to add a conference field in its full length and respective acronyms they are known by as well as the team name that ESPN currently uses. Please note that this reference table is an aggregated view of all of the different conferences a team has been a part of since 2002 and the different team names that KenPom has used historically, so this mapping table is necessary to map all of the teams properly and differentiate the historical conferences from their current conferences. From there, I join a reference table that includes all of the current NCAAM coaches and their active coaching lengths because the active current coaching length typically correlates to a team's success in the March Madness tournament. I also join another reference table to include the historical post-season tournament teams in the March Madness, NIT, CBI, and CIT tournaments, and I join another reference table to differentiate the teams who were ranked in the top 12 in the AP Top 25 during week 6 of the respective NCAA season. After some additional data clean-up, all of this cleaned data exports into the "DEV _ March Madness" file that contains the consolidated view of all of this data.

This dataset provides users with the flexibility to export data for further analysis in platforms such as Domo, Power BI, Tableau, Excel, and more. This dataset is designed for users who wish to conduct their own analysis, develop predictive models, or simply gain a deeper understanding of the intricacies that result in the excitement that Division 1 men's college basketball provides every year in March. Whether you are using this dataset for academic research, personal interest, or professional interest, I hope this dataset serves as a foundational tool for exploring the vast landscape of college basketball's most riveting and anticipated event of its season.

Managing and monitoring agents at scale was the main barrier to putting agentic AI into business production in the APAC region in 2026, at 63 percent. This was higher than in the Americas and EMEA. In contrast, 51 percent of respondents in the Americas cited a shortage of skilled staff as the most significant barrier.

In 2025, the market size change in the 'Natural Language Processing' segment of the artificial intelligence market worldwide was modeled to be 87.32 percent. Between 2021 and 2025, the figure dropped by 24.04 percentage points, though the decline followed an uneven course rather than a steady trajectory. The forecast shows the market size change will steadily decline by 64.36 percentage points from 2025 to 2032.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Natural Language Processing.

When asked about how their organizations are adjusting their talent strategies due to the adoption of artificial intelligence (AI), over half of AI leaders worldwide named educating a broader workforce to raise overall AI fluency in 2025. The second-most common adjustment was designing and implementing strategies for employee reskilling and upskilling.

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

According to a global survey conducted among 52 participating institutions in 2025, there were more participants who viewed AI's impact on education as an opportunity in every region than as a challenge. However, in the USA and Canada, 43 percent of faculty thought AI could be a challenge in education, higher than in every other region. On the other hand, Latin America had the highest share of respondents viewing AI's impact on education as an opportunity, at 78 percent.

The market size in the 'Natural Language Processing' segment of the artificial intelligence market worldwide was modeled to be 126.05 billion U.S. dollars in 2025. Between 2020 and 2025, the market size rose by 104.2 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size will steadily rise by 742.68 billion U.S. dollars over the period from 2025 to 2032, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Natural Language Processing.

The market size in the 'Autonomous & Sensor Technology' segment of the artificial intelligence market worldwide was modeled to amount to 57.3 billion U.S. dollars in 2025. Between 2020 and 2025, the market size rose by 24.52 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size will steadily rise by 46.04 billion U.S. dollars over the period from 2025 to 2032, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Autonomous & Sensor Technology.

The market size in the 'Computer Vision' segment of the artificial intelligence market worldwide was modeled to be 103.13 billion U.S. dollars in 2025. Between 2020 and 2025, the market size rose by 55.05 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size will steadily rise by 130.03 billion U.S. dollars over the period from 2025 to 2032, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Computer Vision.

In 2025, 21 percent of AI leaders surveyed worldwide expected intelligent security systems and smart monitoring to have the greatest impact among physical AI types. Collaborative robotics and digital twins followed closely with a share of 20 percent.

The market size change in the 'Machine Learning' segment of the artificial intelligence market worldwide was modeled to amount to 203.79 percent in 2025. Between 2021 and 2025, the market size change rose by 154.04 percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size change is expected to drop by 192.83 percentage points between 2025 and 2032, showing a continuous downward movement throughout the period.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Machine Learning.

In 2025, India and Nigeria recorded the highest rates of regular or semi-regular artificial intelligence (AI) use, with 92 percent of consumers adopting these technologies. Emerging economies led this trend, as the United Arab Emirates and Egypt following with usage rates of 90 percent. Among advanced economies, Singapore reported a 73 percent AI usage rate in the same period.

The market size change in the 'Autonomous & Sensor Technology' segment of the artificial intelligence market worldwide was modeled to amount to 91.58 percent in 2025. Between 2021 and 2025, the market size change rose by 39.83 percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size change is expected to drop by 85.3 percentage points between 2025 and 2032, showing a continuous downward movement throughout the period.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Autonomous & Sensor Technology.

Saudi Arabia had the highest score for government strategy of AI in 2024, or 100. Following closely behind was the United States with 83.

Generative AI adoption has surged across industries, with the technology sector leading the charge at an impressive 88 percent usage rate across functions in 2024. This rapid integration of AI technologies is reshaping business operations, particularly in marketing and sales, where AI has found widespread application as a creative assistance tool. However, this swift adoption has not come without challenges, as concerns about regulatory compliance have grown in tandem with the increased usage. Varied adoption rates across sectors While the technology industry stands at the forefront of generative AI adoption, other sectors are not far behind. Professional services, advanced industries, and media and telecom all report adoption rates of around 80 percent across functions. Interestingly, in the tech, media, and telecom industry, IT departments lead in generative AI application usage at 34 percent, followed by product development at 17 percent. This trend differs in the energy, resource, and industrial sector, where operations take the lead at 23 percent, with IT following at 17 percent. Evolving landscape of AI implementation As organizations increasingly integrate generative AI, the landscape of implementation is evolving. Automation and agentic AI have emerged as the most intriguing technological developments for organizations in 2024. This shift is accompanied by a notable increase in technical skills related to AI, indicating broader usage. However, the rise in regulatory concerns suggests that governments and authorities are stepping up their oversight of the industry. This dual trend of increased adoption and heightened regulatory scrutiny underscores the complex environment in which AI technologies are being deployed and developed.