For each dataset, it is necessary to determine its quality. Several characteristics describe high-quality data, but it is especially important to point out accuracy, reliability, and completeness. Every high-quality data should be precise and error-free, otherwise, your data is misleading and inefficient. If your data is not complete, it is harder to put the data in use because of the lack of information. What if your data is ambiguous or vague? Basically, you cannot trust your data, it's unreliable.
Data quality is the answer to the question “How is my data?” If your data helps you with business operations and decisions, you can say that your data is of good quality.
By googling stuff like free datasets for machine learning, time-series dataset, classification dataset, etc., you see a bunch of links to different sources. But which of them include high-quality data? We will list a few sources, but it is essential to know that among them there are also data that have their drawbacks. Therefore, you have to be familiar with the characteristics of a good dataset.
Kaggle is a big data-science competition platform for predictive modeling and analytics. There are plenty of datasets you can use to learn artificial intelligence and machine learning. Most of the data is real and referenced, so you can test or improve your skills or even work on projects that could help people.
Each dataset has its usability score and description. Within the dataset, there are various tabs such as Tasks, Code, Discussions, etc. Most datasets are related to different projects, so you can find different models which are trained and tested on the same datasets. On Kaggle, you can find a big community of data analysts, data scientists, and machine learning engineers who can evaluate your work and give you useful tips for further development.
UCI Machine Learning Repository
The UCI Machine Learning Repository is a database of high-quality and real-world datasets for machine learning algorithms. Datasets are well known in terms of interesting properties and expected good results; they can be an example of useful baselines for comparisons. On the other hand, the datasets are small and already pre-processed.
GitHub is one of the world’s largest communities of developers. The main purpose of GitHub is to be a code repository service. In most cases within a project, we can find its application on some datasets; you will need to spend a little more time to find the wanted dataset, but it will be worth it.
data.world is a large data community where people discover data and share analysis. Inside almost every project, there are some available datasets. When searching, you will need to be very precise to get the desired results.
Of course, there are a lot more sources, depending on your need. For example, if you need economic and financial datasets, you can visit World Bank Open Data, Yahoo Finance, EU Open Data Portal, etc. Once you have found your dataset, it’s Graphite time; run several models and create various reports using visualizations and tables. With Graphite, it's easier to make business decisions. Maybe you are just a few clicks away from the turning point of your career. 🙂