Whether you are adding a data project to your portfolio or you’re starting your first project as a paid Data Analyst, your first task will be to find a suitable data set. In this article, we’ll go over the basics of what a data set is, how to find them, and ways to determine their quality.
So, what is a data set?
A data set is simply a collection of information. Usually, datasets are not always organized in a way that is immediately useful, and it will need a bit of work on your part to make it usable. Datasets can come in various forms, such as spreadsheets, databases, JSON files, or even text files. They serve as the foundation for Data Analytics and visualisation.
So where do you find them?
In this article, we’ll highlight a few repositories where you can find data on everything from business to finance and even crime.
Kaggle
Kaggle is one of the largest platforms for data science and machine learning competitions. It hosts a vast repository of datasets across a wide range of topics, including healthcare, finance, social sciences, and more.
– Type of data: Various types of datasets, including structured, unstructured, and time-series data, covering topics such as healthcare, finance, natural language processing, computer vision, and more.
– Access: Users can access datasets through the Kaggle platform by creating a free account and browsing the dataset repository. Some datasets may require participation in competitions or adherence to specific terms and conditions.
UCI Machine Learning Repository
The UCI Machine Learning Repository is a collection of databases, domain theories, and datasets widely used by the machine learning community.
– Type of data: Datasets suitable for machine learning research and experimentation, including classification, regression, clustering, and recommendation systems.
– Access: Datasets are freely available through the UCI Machine Learning Repository website. Users can browse datasets by category, view dataset descriptions, and download data files in various formats.
Data.gov
Data.gov is the official open data portal of the United States government, providing access to a vast array of datasets from federal agencies.
– Type of data: Governmental datasets from federal agencies covering diverse topics such as demographics, economics, healthcare, environment, public safety, and more.
– Access: Datasets are accessible through the Data.gov portal, where users can search, browse, and download datasets for free. Data.gov promotes transparency and collaboration by providing open access to government data.
Google Dataset Search
Google Dataset Search is a specialized search engine developed by Google to help users discover datasets across the web.
-Type of data: A wide range of datasets from various sources, including academic repositories, data providers, government agencies, and research institutions.
– Access: Users can search for datasets using keywords, topics, or data formats through the Google Dataset Search website. The search results provide direct links to the source repositories or websites hosting the datasets.
GitHub
GitHub is a popular platform for software development, collaboration, and version control.
– Type of data: Datasets shared by researchers, data scientists, organizations, and communities on the GitHub platform, covering diverse domains and topics.
– Access: Datasets are accessible through GitHub repositories tagged with “dataset” or by searching for specific datasets using keywords. Users can explore repositories, view dataset files, and download data for free.
Are these quality datasets?
Determining the quality of a dataset is crucial for ensuring the reliability and validity of your analysis. Here are some factors to consider when evaluating dataset quality:
- Research the source of the data and the methods used for its collection.
- Confirm whether the dataset contains all the necessary variables and observations required for your analysis. Incomplete datasets may lead to biased or inaccurate results.
- Verify the accuracy of the data by cross-referencing it with other reliable sources or conducting validation checks.
- Ensure consistency in data formatting, units of measurement, and coding schemes across the dataset.
- Evaluate the relevance of the dataset to your specific research question or analytical objectives.
After choosing a suitable dataset, the next step is to clean, visualise and generate insights. You can learn how to do that by registering for our next training here.
Comment if you want a part 2 with more sites to check out!
