As companies, no matter how big or how small, look for employees who can understand and analyze data, the role of Data Scientist has become the new trend across the globe.
With the developments that India is making, more people are getting interested in data science courses in India. Questions from the very base like what is computer science are being raised for a student from a very early age.
What is Data Science?
The purpose of data science is to optimize and improve an organization’s business objectives. Statistics, mathematics, and machine learning methods are all used by data scientists to achieve their goals. They analyze and identify problems and possible solutions using mathematical and statistical principles and then formulate an end-to-end production pipeline to implement their theories.
It’s a common misconception that data science and machine learning are the same things because the very idea that comes to mind when data science is discussed is “machine learning”. Machine learning is certainly a vital component of data science, but the idea that it is the same as data science is wrong.
The work of data scientists is to analyze data, identify areas that require improvement, and design procedures to optimize business objectives, sometimes using tools from statistics, mathematics, and machine learning.
Data science is being applied to a variety of industries, including ad technology. In this way, they are able to determine which ad placements are well suited for which user segments, thus serving ads that are more likely to be engaged.
Moreover, it goes beyond business intelligence in that all insights are detected in real-time, and actions are executed to provide an immediate impact because it begins with obtaining insights and ends with executing them immediately to deliver results.
Some Hindrances Data Scientists Face
● Huge Amount of Data:
It becomes very challenging even to perform simple tasks such as aggregation and sorting when the data size reaches several petabytes (1 petabyte = 1 million gigabytes). Although new technologies have made handling large data sets more efficient, data scientists still need to ensure that the operations are done efficiently.
● No Hard Rules:
In the real world, there isn’t a definitive textbook on how to do data science. The data scientist is supplied with a wide range of tools and knows a number of ways to handle each problem. In order to fully understand complex problems, the data scientist must approach them from multiple points of view, first from the perspective of the business, second from the mathematical point of view, and sometimes from a brand new angle.
Upon determining those business objectives, the data scientist needs to decide what approach to utilize, from a quantitative standpoint, would be most useful in meeting them. This, combined with the constantly evolving real-world requirements, makes practical data science a very difficult task.
● The A/B test deployment:
Data scientists and software engineers need to work closely together to ensure the AB test is available on a large scale since implementing an algorithm in real-time requires fundamental changes to their platform. A/B testing is a detailed and difficult process spanning feasibility research, a backtest of historical data, and finally deployment of real-time A/B tests.
As a subject area, data science is not only helping businesses understand their markets and make better decisions but also helping them deliver services to their customers more effectively. E-commerce companies aren’t the only companies seeking Data Scientists, today companies of almost every field are hiring them. Data Scientists are high on demand and low on supply, so these jobs are becoming increasingly competitive.