QUANT TRADING? NO, HERE IS WHY DATA SCIENCE IS BETTER.

In 2008, the name 'Data scientist' was coined to address individuals who mine data to solve specific problems.

Daily, volumes of raw data get mined on different platforms using modern tools to find meaning and unseen patterns to execute business decisions. This decision favors the trader's portfolio.

An unchallengeable fact is; Data science is an integral part of any organization or industry in this century. It ranks as the sexiest job of the 21st century by Harvard.

What are the goals of data science?

The goals of data science are the following:

•Prediction: Predictions based on input an example is; maybe the rain will fall or who will win a game.

•Recognition: Recognition of Voice, image, and Facial. A good example is your phone's security.

•Classification: Think about how your Mail service(Gmail or Yahoo mail) can tell a spam message from a non-spam message.

•Automation: Your credit card approval or declining at the store.

•Segmentation: Demographic-based marketing.

What are the processes of data Science?

Falsehood means that the process of data science is easy. The undiluted truth is; It is not simple. But nothing is beyond your mind. So let us get down to it!.

There are five processes in data science:

•Capture: This is the data acquisition stage

•Maintain: This is the stage data is ware-housed.

•Process: This is the stage data gets mined.

•Analyze: This is the stage of Clustering and Classifying data.

•Communicate: This is the final decision made about the data.

What is the relationship between Data science and trading?

An advantage to traders, data science has created a new capacity for critical analysis. But, most traders are not fully exploiting this capacity. Some traders still depend on quant to execute data in place of data science.

In the aggressive world of finance, traders are intimate to the market. They are more practical in integrating data science, while quants often thread from the theoretical point of view.

During trades, data science uses real-time data from unformed and formed sources to seek unseen patterns and tendencies that are beneficial to the trader—this will give the trader a standard clear path to trade rather than trading blindly. Data science is also a pathway for traders to integrate raw data for further modifications in their productivity.

It is general knowledge that the market is fragile and can fluctuate because of its openness— Data science assists traders in managing this volatility by amassing and examining massive amounts of data from past market activity.

Data science is the key to productivity—using data scientists, You will be highly productive in your trade.

What is the distinction between a Data Scientist and a Quantitative analyst?

Quantitative analysts and Data scientists are both miners. The difference is their job description.

A quantitative analyst examines data, identifies trends, formulates data charges, and develops presentations to assist companies in making strategic decisions.

Most of the work is theoretical and requires deep-mathematical skills and knowledge in statistics to comb data.

The duties of quantitative analysts vary across enterprises and companies. But basically, quantitative analysts use data to achieve meaningful intuitions and solve complex issues. In addition, they analyze well-defined data sets with several tools. While resolving business needs like; quarterly sales reports, sales campaign problems, Etc.

Data scientists structure and design data modeling methods and production employing algorithms, prototypes, predictive models, and custom analysis— Data science is more about coding.

The individual must be vast in hacking and detailed technical skills. A data scientist must bring to light the unknown by probing and processing raw data with several tools using a framework and a system.

They are skilled in; Java, Machine Learning, Hadoop, Python, Data mining, and Data Warehousing. Data scientists are usually conducting data modeling procedures.

Devoid of professionals who twirl cutting-edge technology into actionable ideas, big data is a big waste. Besides, data science by Harvard standard is the hottest career in the 21st century. Hence it is the Gem of the new world because it cuts across all industries and companies. Any profit-oriented establishment needs and must use data science to understand its target audience and boost Its ROI.

This article is not to downgrade the integrity of quantitative analysis that has been in existence before data science. But to explain the strength of data science—data science probes Larger data and navigates uncharted waters with machine learning. Machine learning employs artificial intelligence for the calculation and the writing of algorithms. And this makes trade easier for the trader.

In conclusion, data science is a step up from quant trading.