Adventures in Machine Learning

Unleashing the Power of SQL Practice with Real-World Data Sets

The Importance of Finding Cool Data Sets for SQL Practice

SQL (Structured Query Language) is a ubiquitous tool that is used to interact with data stored in relational databases. It is a powerful skill to have in today’s data-driven world.

To improve and maintain proficiency in SQL, the best approach is to practice with real-world data sets. Having access to a database for SQL practice can prove invaluable to anyone seeking to expand their skillset.

A database allows a user to experiment with database design, data manipulation, and SQL queries at any time. Fortunately, there are several sources available when it comes to obtaining free data sets for SQL practice.

Sources for Free Data Sets

There are a variety of sources available when it comes to obtaining free data sets for SQL practice. From government organizations to private companies, there is an abundance of data available from numerous sectors. is a public-facing government website that offers a wide range of data sets that are available for download and use. Data sets on range from climate information to educational and healthcare statistics.

Additionally, FiveThirtyEight is a website that houses numerous data sets that users can use for SQL projects. The website comprises a huge repository of datasets on different subject matters ranging from demographics to climate.

Kaggle is another platform that offers resources to data lovers and enthusiasts. Kaggle hosts a community of data science professionals where users can collaborate and work on real-world data science problems.

Kaggle holds a vast collection of user-contributed data sets ranging from sports data to medical data.

LambdaTest, Airbnb, and IMDb are also examples of organizations that offer data sets for SQL practice.

Lambda Test offers a dataset of ‘most visited websites 2016-2018’, while Airbnb provides a dataset of vacation rentals. The dataset provides access to information relevant to finding the most ideal rentals based on a comprehensive rating system.

IMDb, on the other hand, offers a comprehensive dataset of movie industry information, which includes production time, director names, rating scores, and much more.

Google Trends as a Data Set for SQL Practice

Google Trends offers a rich source of data sets to SQL practitioners. Trends data offers users access to search history data scaled on a timeline, enabling trend analysis over specific periods.

To use this feature for SQL practice, the first step is to visit the Google Trends website and type in a search query. Afterward, targeted search queries can be retrieved to provide insight into how these queries trend over a particular time frame.

For instance, analyzing how much people search for popular streaming services can give useful information as to how they stack up against each other. From Google Trends data, SQL practitioners would also be able to identify the services that have gained in popularity over a specific time.

The data can then be analyzed using SQL queries ranging from filtering data (WHERE clause) to grouping with aggregate functions (GROUP BY).

Filtering and Breaking down Google Trends Data

Google Trends offers a range of filters to improve the accuracy of data yielded. With keyword filtering, data can be filtered to determine specific locations, timespans, search categories (shopping, news among others).

Data filtering makes it easier to extract information that accurately reflects a desired result. Google Trends data can be broken down into various outputs for analysis.

Commonly used data breakdowns include regional interest (by city, state or country), related queries, and categories.

Google Trends List of Trends and Data Visualization for SQL Practice

The Google Trends website provides a list of trending topics based on user queries. The filter section of the website allows users to view currently trending topics at any point.

SQL Practice can also utilize this section of the website to gain insights on the latest trending topics worldwide. Google Trends data can also be visualized using data visualization tools such as Excel, Tableau, and Power BI.

Data visualization enables users to present their results in a more engaging and easy-to-understand format. SQL practitioners can use the visualization of data for their presentations, reports, and articles.


In summary, having a database for SQL practice can make a big difference in improving one’s skills and standing out in today’s data-driven world. The availability of free data sets makes it easier to find relevant data to gain important insights.

With Google Trends as a data set, the trend data can be filtered, broken down, analyzed and can serve as a basis for identifying new opportunities. In further practice, one can utilize visualization tools to create engaging and informative data presentations. for SQL Practice is a U.S. government website that hosts a vast repository of publicly available federal, state and local data. The website was established in 2009, as part of the Open Government Initiative to promote transparency, participation and collaboration.

The datasets provided on are easily accessible for download, and cover diverse topics ranging from education to healthcare and transport.

Using the Search Engine to Find Relevant Data Sets for SQL Practice

To find relevant data sets for SQL practice on, users can make use of the site’s search engine. Users can use the search bar to input a keyword or phrase such as ‘baseball statistics’ or ‘politics’ to retrieve a specific set of data.

Once the search is completed, users will get a tailored list of results based on their keyword. The results can be filtered based on dataset subject, view count, last modified date, or file format to further refine the results.

Accessing Data on Politics and Sports for SQL Practice

The search engine on can easily filter information about politics and sports. For example, it’s possible to filter data on political contributions from lobbyists or access data from the Federal Election Commission.

Local election data, including ballot measures and candidate data, can also be sourced from openly available sources. For sports, offers datasets for many popular sports.

The site provides detailed stats for soccer, basketball, baseball, American football, and many more. These datasets include several data points such as individual player statistics, scores, and results from various leagues and tournaments.

Downloading and Using Datasets in CSV Form for SQL Practice

Data on is available in various file formats, including CSV, XML and JSON to suit the preferences of SQL practitioners. The most preferred format will depend on the intended application of the data set.

However, CSV files are one of the most commonly used file formats for SQL practice. The advantage of CSV files is that they are easily readable and editable as Excel files.

To download a dataset from, users can select `Download` located under the dataset of interest. Users can then select the preferred file format and click `Download`.

Once downloaded, practitioners can open the dataset using a suitable tool, such as Excel, and format the data according to their requirements.

Kaggle for SQL Practice

Kaggle is one of the largest platforms housing data science professionals. It is considered a data lover’s paradise where users can access datasets and collaborate with other data enthusiasts.

Kaggles mission is to make data science more accessible and effective.

Using the Search Engine and Finding Popular Datasets and Materials for SQL Practice

Kaggle provides a user-friendly search engine that allows users to find datasets suitable for SQL practice. Kaggles search engine allows users to filter different search parameters including dataset format, subject matter, and even the number of votes each dataset has received.

Users can also access numerous materials such as tutorials, webinars, and forums, all of which can be used in SQL practice. By participating in Kaggle forums, SQL practitioners can network with other professionals in the industry, share knowledge, and collaborate on real-world data science problems.

Examples of Sports-Related Databases for SQL Practice

Sports-related databases are increasingly becoming popular for SQL practice. Kaggle offers a vast range of sports-related datasets that can be used to develop predictive models based on historic results.

They include soccer stats, baseball statistics, and basketball datasets. These datasets contain the history of scores between teams and game stats of players participating in a particular game.

Participating in Competitions to Improve SQL Skills and Win Prizes

Kaggle offers competitions that are organized to improve data practitioners skills by working on real-world projects. These competitions give participants the opportunity to tackle tough problems and develop predictive/classification models using datasets supplied by the competition organizers.

Competitors earn points based on the accuracy of their predictions. Higher scoring participants stand to take home lucrative prizes.

These competitions are a great way for SQL practitioners to improve their skills and also earn rewards while at it.

Conclusion and Kaggle are two invaluable resources for SQL practitioners. With’s extensive library of data sets of various subject matters, SQL practitioners can easily access datasets and hone their skills.

Kaggle, on the other hand, provides a platform for SQL practitioners to access datasets and collaborate with other data enthusiasts. Additionally, Kaggle competitions help practitioners to improve their predictive/classification modeling skills and earn monetary rewards.

IMDb for SQL Practice

IMDb is among the most notable online resources for movies and TV enthusiasts worldwide. The site offers a platform that allows anyone to discover information about a particular actor, movie, or TV show, ranging from release dates, trivia, filming locations, and cast member biographies.

IMDb also provides a rich dataset of movie-related information that is suitable for SQL practice.

Accessing and Utilizing Different Categories of IMDb Datasets for SQL Practice

IMDb offers several categories of dataset for SQL practitioners. The categories include movie basics, reviews and ratings, endorsements, and movie credits.

IMDb’s datasets also include comprehensive information on a diverse range of TV shows like cast members, episode titles, and production crews.

For example, the movie basics dataset contains film title information such as the title, year of release, rating, budget, and revenue.

The data can be used to create a database or table, which can enable practitioners to manipulate and analyze the data using SQL techniques.

Example SQL Queries Using the IMDb Dataset

Practitioners can create queries to extract and analyze data from the IMDb dataset. An example of a SQL query that can be used to extract an Excel file with a list of top-rated movies based on user ratings can look as follows:


SELECT title, year, rating, votes

FROM programs

JOIN movie_ratings ON programs.program_id = movie_ratings.program_id

WHERE program.type = ‘movie’ AND movie_ratings.rating >= 8.0

ORDER BY rating DESC, votes DESC;


This query creates a new table by joining the programs table and movie_ratings table, consisting of title, year of production, ratings, and votes for each movie. The table would display the movies with ratings that are greater than or equal to 8.0 and order them by the rating, then by the number of votes.

Airbnb for SQL Practice

Airbnb is a popular online platform that offers vacation rental listings available in various locations globally. With a massive database featuring owners, guests, amenities, and room availability details, Airbnb is an excellent source of data for SQL practitioners.

Overview of Airbnb’s Database of Locations for SQL Practice

Airbnb’s location database is vast and contains multiple data region categories such as zip codes, neighborhoods, cities, and countries. SQL practitioners can use Airbnb’s location data to determine the location of a particular property of interest.

Analyzing Property Listings, User Ratings, and Comparing Prices for SQL Practice

SQL practitioners can analyze Airbnb’s property listings, user ratings, and prices to provide valuable insight into the rental market. Analysis can be performed on room type, occupancy rate, and average price trends in particular areas.

Practitioners can isolate any seasonal trends in the preference for various room types, such as analyzing the number of rentals for different accommodation categories during peak and off-peak periods. SQL queries can be used to generate specific outcomes, such as the most commonly booked room type in a particular location, what the average price for a particular room type is, or the occupancy rate at any given time of the year.

Using GIS Data Available on

Airbnb for SQL Practice Projects

Geographic Information System (GIS) data is a valuable source of spatial data in SQL analysis. GIS data on Airbnb allows for more sophisticated mapping applications in SQL projects.

PostGIS is a robust tool that supports the storing and querying of GIS data. PostGIS makes it possible to visually display Airbnb’s spatial data or even determine the distance between two properties using location data with a quick SQL query.

SQL codes written in PostGIS can determine distances between different property locations. This is crucial information for those interested in understanding the impact of location-based variables on rental trends and prices.


Airbnb for SQL practice provides numerous location-based datasets and vacation rental information suitable for SQL queries. Airbnb’s location data is particularly valuable to SQL practitioners, as it offers a platform for identifying trends in vacation and room bookings.

On the other hand, IMDb’s dataset provides excellent movie-related data for SQL practice, including ratings and movie basics. By making use of these datasets and practicing SQL queries on real-world examples, data scientists are better equipped with the tools required to develop data-driven decision-making strategies.

Earthdata for SQL Practice

Earthdata is a NASA-funded atmospheric, environmental, and earth science data management system that promotes data sharing and collaboration for scientific research. Earthdata offers data sets in diverse environmental subject areas, including atmospheric composition, climate science, food security, farming, forest cover, and water.

With over 20 petabytes of archived data, Earthdata is an excellent resource for SQL practitioners interested in environmental data science.

Overview of Earthdata as a Source of Environmental Data Sets

The Earthdata dataset is obtained from various sources, including ground-based observations, field campaigns, and satellite science data. Underpinning the Earthdata’s dataset is a host of sensors and instruments that collect environmental data that supports NASA’s research and innovation goals.

Earthdata offers a wide variety of datasets for SQL practice. The datasets available include atmospheric sciences, biodiversity, and ecosystems, disasters, health and air quality, climate change, and water.

These datasets provide practitioners with a wealth of environmental data that can be used to draw valuable insights using SQL queries.

Accessing and Utilizing Petabytes of Data to Improve SQL Skills

Accessing the relevant data from Earthdata’s database requires some level of expertise to handle and analyze it effectively. For SQL practitioners, the challenge lies in finding petabytes of data that are relevant to a specific research question of interest.

Earthdata addresses this challenge by allowing SQL practitioners to select pre-built analysis workflows to access the most relevant datasets. Practitioners can also access tools and services that aid in data discovery, access, and documentation.

These tools are specially designed to help SQL practitioners identify and manage large volumes of environmental data, ensuring that the data is accurate and of the required quality.

Examples of Environmental Analysis with SQL Queries

SQL queries can be used to extract, analyze and

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