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.

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 2025 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 2025 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. - 2025 Tournament Insights: Contains all seed and region information for the 2025 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 2025 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.

This data table provides the detailed data quality assessment scores for the Network Flow: Power, Current and Embedded Generation dataset. The quality assessment was carried out on the 31st March. At SPEN, we are dedicated to sharing high-quality data with our stakeholders and being transparent about its' quality. This is why we openly share the results of our data quality assessments. We collaborate closely with Data Owners to address any identified issues and enhance our overall data quality. To demonstrate our progress we conduct, at a minimum, bi-annual assessments of our data quality - for datasets that are refreshed more frequently than this, please note that the quality assessment may be based on an earlier version of the dataset. To learn more about our approach to how we assess data quality, visit Data Quality - SP Energy Networks. We welcome feedback and questions from our stakeholders regarding this process. Our Open Data Team is available to answer any enquiries or receive feedback on the assessments. You can contact them via our Open Data mailbox at opendata@spenergynetworks.co.uk.The first phase of our comprehensive data quality assessment measures the quality of our datasets across three dimensions. Please refer to the data table schema for the definitions of these dimensions. We are now in the process of expanding our quality assessments to include additional dimensions to provide a more comprehensive evaluation and will update the data tables with the results when available.

The Business Intelligence (BI) market size was valued at USD 29.42 USD billion in 2023 and is projected to reach USD 53.78 USD billion by 2032, exhibiting a CAGR of 9.0 % during the forecast period. The increasing adoption of cloud-based BI solutions and the growing demand for data-driven insights to improve decision-making are the primary factors driving the market growth. Business intelligence (BI) is the software that feeds on the business data and then presents it in such user-friendly views as reports, dashboard, charts, and graphs. The businesses study the data to obtain actionable insights and inform themselves in decision-making. Business intelligence tools allow users to work with different types of data - historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Through this, users can discover valuable insights as to how the business is doing. Business intelligence is a general name that embraces data mining, process analysis, performance benchmarking, and descriptive analytics. BI not only processes all the business data but also offers reports, performance indicators, and trends that are easily understood by management hence helping to make decisions. Recent developments include: In June 2023, ThoughtSpot, an AI-powered analytics firm, acquired Mode Analytics, a business intelligence company, to expand ThoughtSpot’s presence in India and double the customer base., In May 2023, Qlik acquired Talend for expanding the company’s capabilities for modern enterprises to transform, trust, access, analyze, and take action with data., In January 2023, Microsoft announced Power BI in Microsoft Teams for improved experiences. The announcements come with three new features, rich broadcast cards for Chat in Microsoft Teams, an update for legacy Power BI tabs for Channels 2.0, and listening and learning from experiences and requirements. , In December 2022, Tableau launched its upgraded Tableau 2022.4 for business users and analysts to explore insights. It automates developing, analyzing, and communicating insights with data stories such as Data Change Radar, Data guide, and Explain the Viz., In November 2022, Qlik launched a new cloud-based data integration platform. The advanced platform as a service combines catalog capabilities and data preparation in a single place. The new integration enables real-time data analysis for organizations. The advanced platform includes a range of services that form a data fabric unification of services to connect data sources that allow an organization to have an integrated view of its data., In October 2022, Mode Analytics announced its partnership with Dbt Labs to reveal the launch of the new Semantic Layer of Dbt. Mode Analytics deep integration and Dbt Semantic Layer enables governed, consistent metrics instantaneously accessible for exploration without any code. Thus, it allows organizations to define and manage their key business metrics consistently., In October 2022, Oracle enhanced inclusive and incorporated data and analytics facilities to empower corporate users. With the new abilities in Oracle Fusion Analytics over ERP, CX, HCM, and SCM analytics, corporate users can now use dashboards, KPIs, and reports to evaluate performance over strategic goals.. Key drivers for this market are: Increasing Usage of Integrated BI Systems to Augment the Market Growth . Potential restraints include: Difficulties in Abstracting Data from Third-party Systems due to Poor Data Quality Hinder the Market Growth. Notable trends are: Growing Popularity of Continuous Intelligence to Propel Market Growth.

The size of the US Business Intelligence Market was valued at USD 19942.01 million in 2023 and is projected to reach USD 38369.43 million by 2032, with an expected CAGR of 9.80% during the forecast period. Business Intelligence (BI) refers to the technologies, processes, and practices used to collect, analyze, and present business data in a meaningful way to support decision-making within an organization. BI involves a wide range of tools and techniques, including data mining, reporting, performance management, analytics, and querying, to convert raw data into actionable insights. By integrating data from various sources such as internal databases, external data providers, and cloud platforms, BI enables companies to gain a comprehensive view of their operations, market trends, customer behavior, and financial performance. This growth is driven by factors such as the increasing adoption of data-driven decision-making, the need for real-time insights, and advancements in artificial intelligence (AI) and machine learning (ML) technologies. The market benefits from the integration of BI with other technologies such as cloud computing, big data, and the Internet of Things (IoT). Additionally, government initiatives promoting data transparency and accountability, as well as rising data security concerns, are contributing to the growth of the US Business Intelligence Market. Recent developments include: In January 2023, Microsoft launched Power Bl in Microsoft Teams to enhance user experiences. The announcements include three new features: rich broadcast cards for Chat in Microsoft Teams, an update for classic Power Bl tabs for Channels 2.0, and listening to and learning from experiences and requirements., In December 2022, Tableau released its improved Tableau 2022.4 for business users and analysts to discover insights. It automates the creation, analysis, and communication of insights through data stories like Data Change Radar, Data Guide, and Explain the Viz., In November 2022, Qlik introduced a new cloud-based data integration platform. The sophisticated platform as a service brings together catalog capabilities and data preparation in one place. The new integration enables firms to do real-time data analysis. The advanced platform includes a number of services that combine to form a data fabric, connecting data sources and providing an organization with an integrated view of its data.. Notable trends are: Increased capital infusion promotes market growth.

The Department of the Prime Minister and Cabinet is no longer maintaining this dataset. If you would like to take ownership of this dataset for ongoing maintenance please contact us.

PLEASE READ BEFORE USING

The data format has been updated to align with a tidy data style (http://vita.had.co.nz/papers/tidy-data.html).

The data in this dataset is manually collected and combined in a csv format from the following state and territory portals:

• https://www.cmtedd.act.gov.au/communication/holidays
• https://www.nsw.gov.au/about-nsw/public-holidays
• https://nt.gov.au/nt-public-holidays
• https://www.qld.gov.au/recreation/travel/holidays/public
• https://www.safework.sa.gov.au/resources/public-holidays
• https://worksafe.tas.gov.au/topics/laws-and-compliance/public-holidays
• https://business.vic.gov.au/business-information/public-holidays
• https://www.commerce.wa.gov.au/labour-relations/public-holidays-western-australia

The data API by default returns only the first 100 records. The JSON response will contain a key that shows the link for the next page of records. Alternatively you can view all records by updating the limit on the endpoint or using a query to select all records, i.e. /api/3/action/datastore_search_sql?sql=SELECT * from "{{resource_id}}".

This report presents data about GPs, Nurses, Direct Patient Care and Admin/Non-Clinical staff working in General Practice in England, along with information on their patients and practices. This series of statistics was previously produced quarterly but will now be produced monthly from the publication of September 2020’s data onwards. We will aim to publish these new monthly publications as close to the end of the month following extraction as possible, and a clearer timetable will be produced as the production stabilises. We have been reviewing and rationalising the contents of this series and in our two most recent publications in May and August we published our proposals for the initial changes which have now come into effect. Monthly publications will include as standard the Bulletin tables Excel file and the Individual-level and Practice-level CSV files. We will also aim to update the interactive Power BI dashboard. Twice a year, for March and September data, we will produce additional regional and experimental tables in Excel, and a CSV of Practice Information including contract type. The first of these biannual publications will be of September 2020 data and will be released 26 November 2020. Information on the next phase of changes will be detailed in future publications. We welcome feedback from all our users on these proposals, by email to: PrimaryCareWorkforce@nhs.net. Information about the contents of this publication and how it can and cannot be used, can be found in the Report tab. Our publication includes information on recent and planned changes as well as highlighting future developments. We are continually working to improve our publication to ensure the components are as useful and relevant as possible for our users. In the General Practice Workforce 30 September 2019 publication, released 28 November 2019, we produced a revised General Practice Workforce time series incorporating improvements in the estimation methodology and in the collection of GP registrar and GP locum data. The revised time series figures are not comparable with earlier figures published prior to 28 November 2019 and therefore all the figures released in the General Practice Workforce 30 September 2019 publication and later publications supersede the earlier releases, all of which we have archived and we advise you not to use them. Various data breakdowns are available in the accompanying Excel and CSV files, including time series and breakdowns by characteristics such as age and gender. Data is also presented regionally and at practice level for September 2020 in the accompanying CSVs. This publication also features an interactive Power BI dashboard which allows users to explore the underlying data in a variety of ways. This can be accessed by clicking on the dashboard link below. Links to other publications presenting healthcare workforce information can be found under Related Links.