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In the ever-evolving world of data science, the quest for quality datasets is never-ending. Aspiring data scientists and seasoned professionals alike can attest to the importance of having access to diverse and reliable datasets to fuel their projects.
But where can you find these coveted datasets without breaking the bank or racking your brains?
Fear not, for in this blog post; we have compiled a list of the 11 best sources for free data sets that cater to a wide range of data science projects. We’ve also included some tips for working with free data sets as well as creating some of your own.
Read on for the full detailed list!
What Are The Best Sources for Free Data Sets?
Finding quality datasets for data science projects is the first step towards success, and the good news is that a wealth of free datasets are available online.
Here’s a quick list:
1. Kaggle Datasets
Kaggle Datasets is a widely-used platform for machine learning competitions and provides a broad selection of user-contributed datasets for various data science initiatives, making it a valuable resource for the data science community.
From image classification to sentiment analysis, Kaggle offers an extensive selection of user-submitted datasets for various data science applications.
The benefits of utilizing Kaggle for data sets go beyond its rich repository. It also provides access to smaller, more straightforward datasets, which significantly accelerate the training and modification of models, making it an excellent resource for building a data science portfolio.
Examples of datasets available on Kaggle include the MNIST Database of Handwritten Digits, datasets used in BuzzFeed articles, and personal spending data and order history from Amazon.
2. UCI Machine Learning Repository
The UCI Machine Learning Repository is a comprehensive collection of datasets available for download. The machine learning community mainly utilizes these to evaluate machine learning algorithms empirically.
This well-known source offers clean and ready-to-use datasets ideal for a typical data visualization project or machine learning tasks.
One of the datasets offered by the UCI Machine Learning Repository is the Default of Credit Card Loans dataset, which is sourced from default payments in Taiwan, providing valuable economic and financial data for analysis.
With its reputation for providing clean and ready-to-use datasets, the UCI Machine Learning Repository makes data publicly available for researchers and practitioners, ensuring a seamless experience while working on your data science project.
However, since these are clean data sets, they aren’t that great for learning basic data cleaning skills, which is 80% of a data scientist’s or data analyst’s job.
3. Data.gov
Data.gov is a website that is part of the United States open government initiative, allowing users to access data from multiple US government agencies, making it a valuable resource for exploratory data analysis.
With data ranging from government budgets to school performance scores, Data.gov is a useful source for various data visualization projects.
Registration is not required to view the data sets on Data.gov, making it an accessible resource for any data science project.
However, some data sets require additional steps to be completed, such as agreeing to licensing agreements, which may impact the feasibility of using them in a streaming data project.
Nonetheless, their offline data sets are great for simple data analysis work.
Some of their data file formats include:
- CSV
- XLS
- RDF
- JSON
- XML
- HTML
4. Google Public Data Sets
Google Public Data Sets provides an extensive library of datasets from various sources, enabling users to freely explore and analyze large datasets, such as historical weather data from NOAA weather stations.
To access Google Public Data Sets, you’ll have to sign up for a GCP account, which comes with the first 1TB of queries free of charge.
Additional datasets can be discovered through Google Dataset Search, a search engine they’ve created.
Google Dataset Search aggregates and curates data from external sources, providing a comprehensive overview of the available datasets, including descriptions, providers, and last update dates.
5. Global Health Observatory
The Global Health Observatory is a public health observatory by the World Health Organization (WHO) to share data on global health.
It serves as a “one-stop-shop” for the world’s largest and most comprehensive collection of up-to-date health data, offering free public access through a data repository, making it a valuable resource for a data science project.
The WHO’s Global Health Observation repository is a platform that features a variety of health-related statistics, including those related to HIV/AIDS, vaccination rates, and malaria.
6. NASA Open Data Portal
The NASA Open Data Portal provides free access to earth-science and space-related datasets, which can be employed for a variety of data science projects.
Datasets related to sea level rise, wildfire frequency, and tropical storms, among other earth sciences insights, are available through NASA’s Earth Science Data Systems Program.
For a more unique and challenging data science project, I recommend giving one of these datasets a try
7. US Environmental Protection Agency Environmental Dataset Gateway
The United States Environmental Protection Agency is a federal government agency established to safeguard human health and the environment.
Its Environmental Dataset Gateway offers datasets about environmental issues, including air quality, water pollution, and climate change, making it a valuable resource for a data science project.
Examples of datasets provided by the United States Environmental Protection Agency include air quality data, water pollution data, and climate change data.
8. Google Trends
Google Trends is a tool that enables users to investigate and download search patterns data, which can be utilized for a variety of data analysis activities, making it a valuable resource for a data science project.
With Google Trends, users can identify prevalent topics in a given industry, investigate search queries, and discover trending products.
You can download search trends data directly from the portal to get large datasets on specific keywords. Through this, you can conduct thorough data analysis for search engine optimization.
9. Reddit Datasets Subreddit
The Reddit Datasets Subreddit (r/Datasets) is a community on Reddit where individuals can share, find, and discuss datasets.
It provides users with a range of datasets suitable for data science projects based on the type of project you’re looking for.
By joining this community, you can not only find datasets relating to your data science projects but also post and discuss your own datasets.
You can also make a request for a specific dataset on the subreddit page.
However, the page may not be that well-organized, and finding good datasets may be tough. If you do find one, you’ll be sure to have a messier data set that’s perfect for practicing your data-cleaning skills.
10. HealthData.gov
HealthData.gov is a website that provides access to a comprehensive array of health-related datasets, tools, and applications to assist researchers, entrepreneurs, and policymakers in addressing issues related to health and healthcare, making it a valuable resource for a data science project.
The portal provides a variety of health-related datasets, such as those related to diseases, healthcare facilities, and medical research, making it a valuable resource for a data science project.
11. U.S. Bureau of Labor Statistics
The U.S. Bureau of Labor Statistics is a government agency that compiles and disseminates data on labor economics and employment.
Their BLS Data Finder portal lets you search for datasets related to employment, wages, and economic indicators, which are suitable for various data analysis tasks, including data processing projects and data visualization projects.
Tips for Working with Free Datasets
Working with free datasets can be both rewarding and challenging. To ensure the success of your data science project, I’ve laid out some helpful tips you may like below.
1. Data Cleaning and Preprocessing
Data cleaning and preprocessing are critical steps in preparing datasets for analysis, ensuring accurate and reliable results.
This involves identifying and rectifying errors, inconsistencies, and missing values in the data, as well as transforming the data into a format that is suitable for analysis.
Make sure to do a thorough cleaning of the datasets you’ve found online before conducting any advanced machine-learning steps.
This will prevent any incorrect or unreliable analysis.
Various data cleaning and preprocessing techniques can be employed to improve the quality of your dataset, such as data imputation, data normalization, data transformation, and data reduction.
2. Understanding Dataset Limitations
Understanding dataset limitations is essential for accurate data analysis and interpretation.
Free datasets may be subject to limitations such as limited size, outdated information, lack of documentation, insufficient data, bias, and deficiencies in data measurements.
To ascertain dataset limitations, it is important to assess the data for missing values, outliers, and biases.
Do consider how large your sample size would be for you to conduct a significant analysis of your data.
3. Combining Multiple Datasets
Combining multiple datasets can provide more comprehensive insights and improve the overall quality of data analysis.
The process of combining multiple datasets involves merging data from different sources into a single dataset, which can be accomplished manually or through automated processes.
Merging multiple datasets can provide a more comprehensive and precise analysis through a larger sample size and a wider range of data points.
Merging datasets is also a great skill set to have when working in the data analytics field.
Creating Personal Datasets
In addition to using free datasets, creating personal datasets can also be a valuable approach for data science projects.
By collecting data from personal experiences, scraping data from websites, or examining public datasets, you can tailor your dataset to your specific needs and gain unique insights.
In this section, we will explore various methods for creating personal datasets.
1. Web Scraping
Web scraping is a technique for extracting data from websites, allowing users to create custom datasets for specific data science projects.
You can use automated tools to acquire data from websites and store it in a structured format. This makes for a fast and convenient way to gather large volumes of data tailored to your needs.
Various web scraping tools and techniques are available, such as Scrapy, Beautiful Soup, Selenium, and Octoparse.
Although web scraping comes with potential drawbacks, such as violating terms of service or obtaining incomplete or inaccurate data, it remains an effective and advantageous technique for creating personalized datasets for data analysis.
2. Social Media APIs
Social media APIs, such as YouTube and Facebook, provide access to user-generated data, which can be used for various data analysis tasks.
These APIs enable developers to programmatically access and interact with social media platforms, allowing them to gather data and create custom applications and tools.
3. IoT Devices and Sensors
IoT devices and sensors are pieces of hardware that detect changes in an environment and collect data. They facilitate data transfer between the digital and physical worlds, enabling real-time data sharing.
Examples of IoT devices include smart mobiles, smart refrigerators, smartwatches, medical sensors, fitness trackers, and smart security systems.
You can even find datasets from your own personal data through the sleep trackers you wear at night. These make for good and impactful projects.
Related Questions
Where can I download data for free?
There are a variety of sources for free datasets, such as Kaggle, UC Irvine Machine Learning Repository, and Data.gov. Additionally, you can create your own personalized datasets by scraping data from websites or acquiring data from social media APIs.
What is a good source of free data?
Kaggle is a great source for free datasets. It offers a wide range of data in various categories, such as finance, health, education, and more. Additionally, the U.S. Bureau of Labor Statistics provides access to datasets related to labor economics and employment.
What types of projects can I do with free datasets?
You can use free datasets for various projects, such as data processing projects, data visualization projects, machine learning projects, and more. The possibilities are nearly endless!
Are Google datasets free?
Yes, Google provides a variety of free datasets that can be used for data analysis. These datasets range across various topics such as finance, healthcare, medicine, natural language processing, and more. You can find these datasets on the Google Cloud Platform Google Public Data Sets or access them directly through APIs.
What are the best free datasets for data visualization?
Kaggle is a great source for free datasets that are well-suited for data visualization projects. There are various sources of open-source data, such as Google Trends, NASA Open Data Portal, and Data.gov. Additionally, Google Public Data Sets provide access to various government databases.
All of these datasets can be used to create insightful visualizations. Additionally, there are plenty of datasets on academic sites like arXiv.org or Figshare.
What are some data sets to analyze for projects?
Data sets for projects can be found from a variety of sources, including Kaggle, UC Irvine Machine Learning Repository, and the U.S. Bureau of Labor Statistics. Additionally, you can create your own personalized datasets by scraping data from websites or acquiring data from social media APIs.
Final Thoughts
These are all the places for free data sets that I’ve found to be the most useful.
As you venture into the realm of data science, don’t be afraid to create your own unique datasets as well.
With these tips in mind, you should have no problem finding the right free dataset for your data science project.
I hope this article helps you in your search to find free datasets!
