Any big data project demands large amounts of data to turn out to be effective. If you’ve ever tried your hands on a data science project, you most likely know how hard it is to find the right data sets from the web. More often than not, the data sets that you need aren’t available on the web for easy download. Even if you manage to find the right ones, the chances of the data sets being structured and clean are slim.
Although cleaning the data can be considered an integral part of a data science, it’s always better to look for a clean and ready-to-use data set that can cut down your efforts and leave you with more time to focus on the analysis part.
You can always get data sets from some open repositories like Kaggle, Google Public Data sets and AWS Public Data sets. The problem however is, these can only be used for generic or testing projects where you need some sample data. When your data project is unique and result oriented, you need data from fresh and relevant sources in a usable condition. Finding reliable and relevant data sets from the web is like looking for a needle in a haystack.
What should an ideal data set be like?
Cleaning up or fixing messy data sets is not what the dreams of a data scientist are made of. To ensure that you don’t block your time doing the repetitive and boring task of fixing data sets, you should simply look for the ‘fantastic data sets’. Here are some pointers that can help you evaluate data sets:
- The data set shouldn’t be messy since you wouldn’t want to waste your time cleaning it up
- There shouldn’t be too much missing data
- The data should be interesting and nuanced enough to be analyzed
- It should be properly structured with a machine-readable syntax
- Column names should be self-explanatory so as to avoid confusion and improve the clarity
- It shouldn’t have duplicate records
- The data set shouldn’t have an irregularly high number of rows and columns as this could slow down the analyses
As a web crawling company, we understand the need for clean and structured data sets for projects that range from market research and data visualization to AI training and Natural language processing. This is why we came up with DataStock, a huge repository of pre-crawled data sets from domains like Retail, Travel, Real Estate, Job, Automobile, Restaurant and more. These data sets are extracted directly from leading websites with high precision web crawling and further processed to make them clean and structured. This makes it an ideal solution for data enthusiasts and businesses in need of ready-to-use data sets. Since these data sets have gone through different stages like deduplication, noise cleansing and structuring, the only thing left for you to do with the data is to plug it to your analytics system; it’s that simple.
Can DataStock help you?
It doesn’t matter if you’re just tinkering around with a new data visualization tool like Tableau or caught up with a critical market research in the e-commerce industry, you will need to source reliable data before you start. The ready to use data sets on DataStock can help you if you are:
- Trying to prototype a data analysis algorithm
- Benchmarking performance on a big data engine like Spark
- Tinkering with a data visualization tool like Tableau or Qlikview
- Doing a market research
- Looking for training data for machine learning algorithm
- Building a text corpora for Natural language processing
As anyone familiar with big data knows, getting hold of good web data sets can be tough if you lack the resources and expertise to run a web crawling set up, in-house. Although there are data sets available in the public domain, many are outdated, poorly structured and need processing before applying in a big data project. On top of this, most businesses are hesitant when it comes to sharing data from their data warehouses. DataStock aims to help businesses find fantastic data sets without having to look everywhere on the web. Our clients from Market research and Machine learning spaces are already reaping the benefits of ready-to-use data sets from DataStock.
To put it in simple terms, DataStock solves two of the biggest problems associated with enterprise-grade data sets – relevancy and usability.