Learn more about the use of data science in cryptocurrency blockchain:
Corporate giants such as Facebook, Google, Apple, and Amazon are mining volumes of data every day.
The vast field of data science has spurred the demand for data scientists who are tasked with deriving meaning from data.
This demand is also fed by the area of big data, an advanced area of data science which deals with extremely huge volumes of data.
With blockchain, a new way of handling data is possible.
It has eliminated the need for the data to be brought together and has paved the way to a decentralized structure
where data analysis is possible right from the edge of individual devices.
Data generated through blockchain is validated, structured and immutable.
Today, most businesses are looking towards deeper, advanced analytics as data has become more accessible and robust.
Currently, the data that businesses use are mostly scattered which demands weeks or months of effort to sort out.
The integrity of the data can be affected greatly by any sort of human error, affecting the end analysis.
Data also faces the risk of being compromised when it is stored in one centralized location.
There is also the possibility of data centers being tampered with and getting released to the public.
Everyone wants needs, but it is a huge chore to ensure that it is accurate and secure.
For executing data analysis and predictive modeling, data science needs a functional and solid data set.
With a decentralized blockchain, data scientists can strengthen their ability to manage data and also set a solid infrastructure.
A straightforward utilization of big data and data science in the crypto space is to perform cryptocurrency analytics.
Big data infrastructure can handle the massive volume of cryptocurrency data generated from transactions.
Data science techniques can generate useful investment insights and predict future outcomes.
By taking transaction data for analysis, it is possible to identify the price fluctuation of any given crypto,
Enabling investors to improve profitability and prevent substantial losses.
Crypto forecasting can also be trained using social-based data. Information like user activities and participation, combined with transaction data etc.