How Much Does Machine Learning Matter in Data Science?
Data Science and Machine Learning are mostly used synonymously; most people also believe one is a trendy word for another.
Data Science is in some sense an umbrella of techniques used to extract information and get better insights into the available data. The range of this type of analysis varies from something as elementary as MIS reports on the one hand and on the other, an intense scientific approach where techniques such as getting inferential analysis, predictive analysis, descriptive analysis, exploratory analysis and so on are considered.
Machine Learning can be explained as an essential part of Artificial Intelligence. Machine Learning empowers the computers to get into a self-learning mode, eliminating the need for overt programming. With the help of new data being fed into the system, these computers can then learn information, adapt to the required changes, and learn and develop all by themselves. They are not human dependent for improvement. Automation of the later part of data mining can be called as Machine Learning.
Machine Learning is not a new term, it has been around for a while, some common applications include, web search, spam filters, credit scoring, online recommendation engines, cyber fraud detection or some advance recent development like the automated google car, however, the ability to automatically learn and develop and apply mathematical calculations to the big data is only currently getting impetus.
Why does Machine Learning matter?
Like all fields which aid development, Machine Learning is also constantly evolving. And as a custom approach to development comes the rise in importance and the demand. One can say Machine Learning is imperative to Data scientists as it helps them drive high-value predictions that can help arrive at better decisions and help take the right actions most importantly in real time, to be effective, and to do this with as minimal human intervention as possible. It eases the task of the data scientist in an automated process and hence is gaining a lot of importance.
Availability of massive data increases the difficulty in analysing it, hence increase in data is directly proportionate to the problems associated with bringing in predictive models that work appropriately. You see a statistical analysis is limited to understanding samples that are static, as a result with time it could give inaccurate conclusions or solutions.
As a knight in shining armour enters Machine Learning which is able to give good solutions to analysing the data in huge volumes. Machine Learning is a leap forward from other available applications like statistics, computer science, etc.., Machine Learning will help produce real results and analysis through the development of effective and efficient algorithms and data-driven models for real-time processing of data.
Machine learning and Data Science will be partners working together. This is the ability of the machine to gain knowledge from data, so without the data, there is very little that machines can learn to do. Thus, it gives a push to get valuable data in order to get valuable and accurate solutions or predictions. So the increased use of machine learning will act as a catalyst to give higher importance to data science. In future, basic levels of machine learning will become a standard operating for a data scientist.
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