Users can access data about cancer statistics in the United States including but not limited to searches by type of cancer and race, sex, ethnicity, age at diagnosis, and age at death. Background Surveillance Epidemiology and End Results (SEER) database’s mission is to provide information on cancer statistics to help reduce the burden of disease in the U.S. population. The SEER database is a project to the National Cancer Institute. The SEER database collects information on incidence, prevalence, and survival from specific geographic areas representing 28 percent of the United States population. User functionality Users can access a variety of reso urces. Cancer Stat Fact Sheets allow users to look at summaries of statistics by major cancer type. Cancer Statistic Reviews are available from 1975-2008 in table format. Users are also able to build their own tables and graphs using Fast Stats. The Cancer Query system provides more flexibility and a larger set of cancer statistics than F ast Stats but requires more input from the user. State Cancer Profiles include dynamic maps and graphs enabling the investigation of cancer trends at the county, state, and national levels. SEER research data files and SEER*Stat software are available to download through your Internet connection (SEER*Stat’s client-server mode) or via discs shipped directly to you. A signed data agreement form is required to access the SEER data Data Notes Data is available in different formats depending on which type of data is accessed. Some data is available in table, PDF, and html formats. Detailed information about the data is available under “Data Documentation and Variable Recodes”.

This information is the "Marriage Overview" statistical monthly indicator data of the Chiayi City Statistical Database query system of the Directorate-General of Budget, Accounting and Statistics, with monthly statistics starting from 2014.

The SQL Query Builders market has emerged as a pivotal segment in the world of database management and development, catering to the increasing need for efficient data handling across industries. These tools enable developers and analysts to construct SQL queries through user-friendly interfaces, thereby streamlining

Context

This project is my first database creation. Taking real-life data from TrueCar.com listings, scraped and posted publicly by another Kaggle user, I attempt on my own to create, preprocess, and scrutinize the data, first by building a schema to format a database in PostgreSQL13 and running several queries based on self-designated questions. Using Jupyter Notebook, I then run the data through Python’s pandas and Scikit learn packages for basic regression analysis. Finally, I created a dashboard via Tableau Public for helpful visualizations.

Content

The dataset shares all but one added column with its original: Region. The original columns include id, price, year, mileage, city, state, vin, make, and model. The addition of the Region column was a self-assigned SQL task: after the original file was uploaded into SQL, I created a new table "Regions" in the database. This data is used to visualize sales across six regions of the U.S.: Pacific, Rockies, Southwest, Midwest, Southeast, and Northeast. City and State were combined in a new column to see data to unique cities, in cases where cities share the same name with others (e.g. Pasadena, Arlington, etc.).

PostgreSQL | See my Database Creation Notes here. Python | See my notebook for performing simple analysis. Tableau | A dashboard can be found in my Tableau Public profile.

Acknowledgements

The dataset utilizes a .csv file extracted from www.TrueCar.com, scraped by Kaggle user Evan Payne (https://www.kaggle.com/jpayne/852k-used-car-listings/data?select=tc20171021.csv).

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

This information is the "Disability Population Statistics" annual data of the "Chiayi City Statistics Database" query system, and is compiled annually from 2000.

WONDER online databases include county-level Compressed Mortality (death certificates) since 1979; county-level Multiple Cause of Death (death certificates) since 1999; county-level Natality (birth certificates) since 1995; county-level Linked Birth / Death records (linked birth-death certificates) since 1995; state & large metro-level United States Cancer Statistics mortality (death certificates) since 1999; state & large metro-level United States Cancer Statistics incidence (cancer registry cases) since 1999; state and metro-level Online Tuberculosis Information System (TB case reports) since 1993; state-level Sexually Transmitted Disease Morbidity (case reports) since 1984; state-level Vaccine Adverse Event Reporting system (adverse reaction case reports) since 1990; county-level population estimates since 1970. The WONDER web server also hosts the Data2010 system with state-level data for compliance with Healthy People 2010 goals since 1998; the National Notifiable Disease Surveillance System weekly provisional case reports since 1996; the 122 Cities Mortality Reporting System weekly death reports since 1996; the Prevention Guidelines database (book in electronic format) published 1998; the Scientific Data Archives (public use data sets and documentation); and links to other online data sources on the "Topics" page.

Database for statistics on higher education (DBH) collects information about the activity at Norwegian universities, university colleges and vocational schools. The database contains information about education, research, employees, finances, areas etc. and is managed by the Directorate for Higher Education and Competence (HK-dir). The information constitutes a statistical bank where data can be retrieved programmatically via API or reports via screenshots, as well as as a special order upon request. There is a client that is linked to API and can be used for testing or ad hoc queries: https://dbh.hkdir.no/dbhapiklient/ The StatBank is divided by subject and table. Within each table, the user can create their own query. The query is designed in JSON format and can be tested in the client. Data is provided as CSV or JSON. Transfer is done via HTTPS or via the client. Data can be retrieved as a sample via the query, or as a whole data set (bulk data). Table 1 in the client provides an overview of the content of the API. Documentation: https://dbh.hkdir.no/static/files/dokumenter/api/api_dokumentasjon.pdf

The Relational In-Memory Database market is experiencing significant growth, driven by the increasing need for real-time data processing and analytics across various industries. These databases store data in the main memory rather than on traditional disk drives, allowing for lightning-fast query responses and highe

This information is the "Population Age Distribution" statistical monthly indicator data of the "Chiayi City Statistical Database" query system of the Directorate General of Budget, Accounting and Statistics, starting from 2009.

Descriptive Statistics for the Boxplots of Fig 2.

This information is the monthly crime statistics data of the "Chiayi City Statistics Database" query system by the Directorate General of Budget, Accounting and Statistics, and has been compiled on a monthly basis since 2014.

Statistics of the ORBDA source database content at the dataset and patient levels.

European Football Leagues Database 2023-2024

Overview This dataset provides comprehensive information about the top 5 European football leagues for the 2023-2024 season. It includes detailed statistics about matches, players, teams, coaches, referees, and more, making it an invaluable resource for sports analysts, researchers, and football enthusiasts.

Dataset Description Leagues Covered: - English Premier League - Spanish La Liga - German Bundesliga - Italian Serie A - French Ligue 1

Database Schema

The database follows a normalized schema design with proper relationships between tables. Here's a simplified view of the main relationships:

leagues
 ↑
teams → matches ← referees
 ↓     ↑
players   scores
 ↑
coaches

Usage Examples

SQL Queries

Here are some example SQL queries to get you started:

1. Get all matches for a specific team: sql SELECT m.*, t1.name as home_team, t2.name as away_team FROM matches m JOIN teams t1 ON m.home_team_id = t1.team_id JOIN teams t2 ON m.away_team_id = t2.team_id WHERE t1.team_id = [team_id] OR t2.team_id = [team_id];

2. Get current league standings: sql SELECT t.name, s.* FROM standings s JOIN teams t ON s.team_id = t.team_id WHERE s.league_id = [league_id] ORDER BY s.points DESC;

3. Get top scorers: sql SELECT p.name, p.team_id, COUNT(*) as goals FROM scores s JOIN players p ON s.scorer_id = p.player_id GROUP BY p.player_id, p.name, p.team_id ORDER BY goals DESC;

Python Example

import pandas as pd
import sqlite3

# Connect to the SQLite database
conn = sqlite3.connect('sports_league.sqlite')

# Read data into pandas DataFrames
matches_df = pd.read_sql('SELECT * FROM matches', conn)
players_df = pd.read_sql('SELECT * FROM players', conn)
teams_df = pd.read_sql('SELECT * FROM teams', conn)

# Analyze data
team_stats = matches_df.groupby('home_team_id')['home_team_goals'].agg(['mean', 'sum'])

Applications

This dataset can be used for: 1. Match outcome prediction 2. Player performance analysis 3. Team strategy analysis 4. Historical trend analysis 5. Sports betting research 6. Fantasy football insights 7. Statistical modeling 8. Machine learning projects

Data Files:

1. matches.csv

   • Match ID, Date, Home Team, Away Team
   • Final Score, Half-time Score
   • Stadium, Referee
   • League and Season information

2. players.csv

   • Player ID, Name, Position
   • Date of Birth, Nationality
   • Team affiliation
   • Personal details

3. teams.csv

   • Team ID, Name, Founded Year
   • Stadium information
   • League affiliation
   • Coach information
   • Team crest URL

4. coaches.csv

   • Coach ID, Name
   • Team affiliation
   • Nationality

5. referees.csv

   • Referee ID, Name
   • Nationality
   • Matches officiated

6. stadiums.csv

   • Stadium ID, Name
   • Location
   • Capacity

7. standings.csv

   • Current league positions
   • Points, Wins, Draws, Losses
   • Goals For/Against
   • Form and Performance metrics

8. scores.csv

   • Detailed match scores
   • Goal statistics
   • Match events

9. seasons.csv

   • Season information
   • League details
   • Year

10. sports_league.sqlite

    • Complete database in SQLite format
    • All tables and relationships included
    • Ready for immediate use

Data Quality

• Data is sourced from football-data.org API
• Regular weekly updates
• Consistent format across all leagues
• Complete historical record for the 2023-2024 season
• Verified and cleaned data

License

This dataset is released under the Creative Commons Zero v1.0 Universal license

Updates and Maintenance

• Dataset is updated weekly
• Last update: March 20, 2024
• Check the version history for detailed changes

Contributing

If you find any issues or have suggestions for improvements, please: 1. Open an issue on the dataset's GitHub repository 2. Submit a pull request with your proposed changes 3. Contact the maintainer directly

Acknowledgments

• Data provided by football-data.org
• Community contributions and feedback
• Open-source tools and libraries used in data collection and processing

Github

Project: https://github.com/kaimg/Sports-League-Management-System

The Quick Stats database is the most comprehensive tool for accessing agricultural data published by NASS. It allows you to customize your query by commodity, location, or time period. You can then visualize the data on a map, manipulate and export the results, or save a link for future use.Data is available By StateBy SubjectCrops and PlantsDemographicsEconomic and PricesEnvironmentalLivestock and AnimalsResearch, Science, and TechnologyThere are 12 data files, each in gzip'd TSV format.

This information is the "Land Population" statistical monthly index data of the "Chiayi City Statistical Database" query system of the Directorate General of Budget, Accounting and Statistics, and has been compiled monthly since 2014.

This dataset consists of the raw data used for creating a frequency distribution of the number of cancer-related papers associated with 20 food ingredients. Paper counts were collected from the Altmetric database from queries in the format of: ingredient AND (cancer OR carcinogenesis OR oncology OR tumour OR tumor). The histogram appears in a blog post on the Altmetric blog.

Agro-MAPS consists of selected agricultural land-use statistics (crop production, area harvested and crop yield) aggregated by sub-national administrative districts for selected years. The full Agro-MAPS database currently contains data for 134 countries - 130 countries at admin1 level; 59 countries at admin2 level. These countries represent 92% of the world land surface. Users can interactively query and display Agro-MAPS data as maps, for a given country or region (Africa, Asia, North America, Latin America & the Caribbean, Asia, Near East in Asia, Oceania).

The World Bank EdStats All Indicator Query holds over 4,000 internationally comparable indicators that describe education access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to vocational and tertiary education. The query also holds learning outcome data from international and regional learning assessments (e.g. PISA, TIMSS, PIRLS), equity data from household surveys, and projection/attainment data to 2050. For further information, please visit the EdStats website.

For further details, please refer to https://datatopics.worldbank.org/education/wRsc/about