DATA SCIENCE VS MACHINE LEARNING
Whenever we are asked about data science or machine learning, we generally get confused with both things or misinterpret. Indeed machine learning is a part of Data Science. Still, in companies, two different persons are hired for both the work, so it justifies merely that both the systems work differently and have an entirely different working process.
So, if to answer a simple question that What is Data Science and Machine learning?
So, Data science is simply a system of data analyzing, that collects the data from different sources, analyze it automatically, and leads to a conclusion. Generally, data scientists are hired by big business companies, as it is quite challenging to calculate a large amount of data by the big companies manually, so here data scientist uses R programming (a language that statistically and graphically analyzes a large amount of data) and python (a programming language that widely used in AI and machine learning) to investigate a large amount of data of different companies.
Now, let’s move to machine learning; machine learning also collects the data from different sources and analyzes it, and leads to technical conclusions, but there are slight differences. The significant difference is that machine learning depends on the data not considered statistically. Still, instead, it analyzes the humans’ behavioral information, it analyses us from the ratings we give while purchasing online, dating apps or software while downloading them, and more or less it analyses the data from the online platform we use, like for watching videos, listening to songs and after analyzing that it shows us the similar results when we use those apps in future.
As both these analyzing formats are essential for all types of business companies, they can analyze the data, and these data can help improve the profit share of the companies and help avoid wastage of material and over-employed worker least employed working areas.
Both these data collectors are co-related to each other; some companies with low turnovers ask their machine learning engineers to work for Data science and vice-versa. However, for high performing companies with a massive amount of data to operate with, they hire different employees in different fields.