Some free datasets of note include Zillow Real Estate Data and Federal Reserve Economic Data. However, there are numerous premium datasets available as well. This is a great data source for a real estate data science project. Each week, Jeremy Singer-Vine compiles a newsletter of useful and curious datasets.
See the Data Pathfinders tool to learn how to source and access science datasets. Datahub is a wonderful source for open data. Jump to the Collections tab to browse datasets in various categories covering everything from climate change to football. You can also use the Find Data tool to search for relevant datasets.
For open data related to crime and law enforcement, this is one of the best sources for U. crime statistics. You can search by state or jump into various datasets including use of force or arrests.
Looking for a specific type of data for your project? Whether you want to work with predictions or classification, these datasets are both interesting and helpful for machine learning projects.
The data is relatively clean and lends nicely to machine learning, e. This dataset, used in DoorDash data science take-homes, features user and transaction data and asks you to build a model to predict the time for deliveries.
Build a stroke prediction model with this handy dataset. The CVS contains patient information like gender, age, pre-existing conditions, and smoking status that can help you build a model. The dataset used in this take-home contains all pitches from the MLB season and asks you to build a model to predict the probability of a type of pitch.
This dataset from the UCI Machine Learning Repository contains survey data from married couples. Use the data to identify predictive indicators of divorce or to build a divorce prediction model. The data used in this take-home includes cyber security threats and events faced in the healthcare industry.
You can use this data to build a healthcare risk assessment model. With data from more than , flights in January and January , this data from the Bureau of Transportation is well suited for building a model for winter flight delays.
This is a useful dataset for a regression data science project. Build models to answer that question with this dataset, which contains information on more than 20, Twitter users. This classic dataset from UCI is a great source for a classification data science project.
One great project idea is to build a model to identify classifiers for poisonous mushrooms. This is a great dataset for a financial prediction model. Use water quality metrics from nearly 4, bodies of water to predict whether the water is safe for consumption or not.
This dataset features ratings for submitted New Yorker caption contest entries. Get some ideas for using this data here.
There are numerous movie rating datasets available here, including one featuring 25 million ratings, making it a great source for building a recommendation engine.
This open dataset covers health inspection scores for restaurants in San Francisco. This dataset is useful for demand forecasting projects.
It contains bike rental data from a bike sharing program, including travel duration, departure and arrival locations, and weather data. With more than 20, images of cats and dogs, this is one of the best datasets for beginner image classification projects.
The MNIST dataset is a large database of handwritten digits. This popular dataset contains information about different types of iris flowers and their characteristics, such as petal length, petal width, and sepal length.
The goal is to predict the species of the iris flower based on these characteristics. Build data visualization projects with these helpful datasets. This dataset includes revenue and sales data from Supercell and asks you to create a visualization of a single aspect of the data that you find important.
With more than 11 million nodes and 85 million edges, this dataset is useful for building graphical relationship models of X users. This is a great dataset for visualizing hotel bookings.
Design visualizations that show top authors, best-selling titles, and review ratings for the best-selling books on Amazon. Visualize the impact COVID is having on hiring with this dataset from the Amazon Open Data Registry.
Its updated polling data is great for visualizing averages and polling movements. This dataset is useful for Matplotlib visualizations. You can create visualizations of exchange rates and currency valuations over time. The dataset features more than 20 years of daily exchange rate data.
There are more than , records in this dataset, featuring daily circulation for the San Francisco library system. You can build visualizations related to new acquisitions, most checked out authors, most checked out titles, etc. This Kaggle dataset features daily trending video data from YouTube.
This dataset features more than 31 years of unemployment for numerous countries around the world. There are a wide range of visualizations you can create, including comparisons of countries, unemployment rates over time, or countries with the lowest unemployment.
This dataset originated on New York State Open Data and features information by station, line, location, etc. You can use this dataset to build visualizations of popular lines or subway maps.
Say you want to take a big dataset and investigate. As you start to dive into the data, you begin to discover patterns, trends, and anomalies. These datasets are perfect for exploratory data analysis projects because they contain large amounts of mostly clean data.
This Airbnb dataset, which is part of a sample data analytics take-home, contains user information for bookings in Brazil. A fun dataset to explore, and great for beginners, this features all of the Netflix original movies up to June 1, and their corresponding IMDb scores.
This Stripe dataset, which features product usage and marketing data, is perfect for diving into marketing and product analytics to determine how well a product is performing.
Featuring 4 years of data from a superstore, this dataset is perfect for analyzing and identifying trends, as well as sales forecasting.
This sample dataset from a Home Depot data science take-home can be used to produce a gross sales forecast for a new product launch. A great source for a marketing analytics project. This is a great dataset for surfacing actionable insights for animal shelters, including what factors led to successful outcomes for the animals.
Another FiveThirtyEight dataset, this one features survey data from non-voters in the U. A few project ideas are identifying key factors that result in non-voting or building a voting likeliness model. A sprawling dataset from Amazon, the Common Crawl corpus features crawling data from billions of websites.
Check out the Example Projects page for ideas. This is a useful dataset for a sports analytics project. Featuring data on more than 20, matches, as well as individual stats from to , this is great for exploratory data analysis projects on line-ups, team stats, wins, and individual player stats.
This large-scale dataset, which was originally developed in , features product information for more than , food items. Data includes allergens, ingredients, and nutrition facts, and there are a wide range of data analytics projects you can do with it.
The survey asked which social platform has influenced your online shopping the most. This is a great dataset for working in Google Analytics or analyzing website traffic. This dataset features more than 20 million metrics on Uber pickups in NYC in and This is great for an exploratory data analysis or analytics project, and you can gather insights into popular pickup locations, common trip routes, and the locations with the longest pickups.
This dataset is a great source for a campaign budget optimization project or for diving into an exploratory data analysis for marketing analytics projects.
This dataset contains a wide range of economic and social indicators for countries around the world, including information about their GDP, population, and education levels.
This dataset contains salaries for roles in the data science field for the year You can group the data by domain, years of experience, and even by country of employment, allowing many angles for exploratory analysis. There are plenty of large datasets great for sentiment analysis and natural language processing NLP projects.
Data like movie reviews, tweets, Reddit comments, and more are all great for these types of projects. This take-home provides a dataset of human vs bot texts and asks you to build a classification model to correctly label the data.
An interesting dataset for performing sentiment or text analysis, this features thousands of posts from the popular subreddit Vaccine Myths. Explore thousands of hotel reviews from TripAdvisor and build semantic prediction or top clustering models.
Another helpful medium source, this features headlines from nearly 20 years. With more than 40, reviews from three Disneyland locations, this is a great data source for performing sentiment analysis. This dataset, which is a classic that was produced in , features star ratings for numerous Amazon products.
The Stanford Sentiment Treebank contains more than 10, Rotten Tomatoes files and provides sentiment annotations on a point scale. This dataset features thousands of airline reviews on X Formerly Twitter from February The data has already been classified as positive, negative, or neutral, and in some instances, includes a reason for the negative tweet.
Featuring 25, movie reviews, you can use this dataset for a binary classification project or to analyze movie review ratings by title. The VoxCeleb large-scale dataset features audio-visual data from 7, speakers. There are about images in this dataset of people wearing facemasks.
You can use this to build models to detect if someone is wearing a mask, not wearing a mask, or wearing a mask improperly. This rich visual-text dataset is loaded with helpful information.
Use the photos for object detection. A bonus: there are millions of keywords and metadata you can use for exploratory data analysis projects as well.
Build a model to detect pathologies and see how well your model performs against radiologists. There are thousands of images of Pokemon characters in this dataset.
Use the data to build a prediction model to determine the type of Pokemon based on the image. This is one of the best datasets for performing object recognition tasks.
Featuring more than 20, photos of dogs, this is a useful dataset for building classification models or a dog breed image classifier project.
Featuring more than 5, images with fine annotations, as well as 20, images with coarse annotations, this is one of the best datasets for understanding urban street scenes at the pixel level. This is a smaller dataset, featuring images of 11 subjects.
Similar to the MNIST handwritten text dataset, this image set includes a training set of 60, images of articles of clothing along with a test set of 10, images. They are a great resource for any professional who works with sound!
It was the equivalent of what punk rock would have been in the 70s, a movement or a lifestyle. These websites all offer music samples from a variety of genres that you can download for free.
Many of them are also available under the creative commons license, which means you can edit and adapt them freely. Looperman is a great place to start, with over , entries in their database and new uploads every day.
From acapella to vocals, rap, and spoken word , their catalog of genres is diverse, and they offer free music software including virtual instruments and plugins to help bring your ideas to life.
Featuring more than 30, clips spanning the last years, BBC Sounds Effects includes clips from around the world that have been created for their radio and television productions. You can also access recordings from the Natural History Unit , and mix and share your tracks using their Mixer Mode function.
NASA App Project Manager said: "NASA has been making historic sounds for over 50 years. Now we're making some of these memorable sounds easy to find and use. A digital magazine for music producers, Bedroom Producers Blog , offers information on all aspects of music production , from plugins to sound design tools, as well as samples.
When SampleSwap first started back in , it was a folder of audio samples belonging to founder and curator Canton Becker. Two years later, he shared them online and has been hand-selecting submissions from other creatives to share with the world ever since.
Reddit has established itself as a popular resource for news, advice, reviews, and more on a huge range of topics, including music. Freesound is another great site for audio snippets and samples, all of which are available for reuse. What makes this site unique, is its search capability.
Shutterstock is well known for its library of free and paid-for images that people can use for their graphic materials, but what you might not have known is that they also provide free music samples for your audio projects. See trends, search by mood and category, and read about everything from audio terminology to recommended headphones via their blog.
You can explore their 80, entries alphabetically along with their latest addition — a collection of almost free tape-treated samples, perfect for adding depth and atmosphere to your next composition. Splice is a subscription service that gives you access to millions of resources, from royalty-free images to presets and music samples.
While it might not be completely free, Splice does offer a day free trial that allows you to download individual tracks, as well as packs organized by artists, producers, and sound designers. You can also cancel your subscription at any time.
If you want to learn how to put these samples into practice, Domestika's online music courses cover everything from software to music production and composition, all taught by industry-leading creatives.
Explore these resources and learn how to incorporate these free music samples into your next creative project:. New to the world of music? This course teaches you the basics of composing a piece: Introduction to Music Production.
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