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Clarifying the Classic Confusion between Data Science and Machine Learning

 

Some say that Data Science and Machine Learning are the same while some others don’t agree on that. Some say they have similarities and some others say that they are vastly different. This confusion has been prevalent all across the web. So, since you have landed here and are reading this article, there is a possibility that you are struck amidst this confusion too. So we have jotted down a few pointers to help you clarify this classic confusion. Keep reading to learn what we have to say and mind you, don’t lose your train of thought! So let’s get started.


Firstly, let us begin the comparison by understanding the two terms: Data Science and Machine Learning.

Data Science is the process of processing large chunks of data and churning it into useful information to extract meaningful insights. But if you are a data science newbie and do not understand the term data processing, let us elaborate. 

Let us assume that the problem statement at hand is “Understanding climate change and predicting future climate”.

In this case, previous data on climate and weather is collected, the data is organised into a crystal clear format and the unwanted data is removed leaving behind only the required data. This structured data is later analysed to find patterns in climate changes over a period of time. Using these patterns and insights, future climate is predicted by applying the resultant patterns to the current data. This complete process is Data Science.

On the other hand, Machine Learning is the process of building models that can automatically extract required insights and predictions from the data that is fed to these models. 

If we consider the previous scenario, the data that is extracted and filtered is fed to the machine learning model. These models are built based on the problem statement at hand using appropriate algorithms and mathematics. The model is further trained using previous data until it makes predictions with moderate to high accuracy. The model is then fed with the current data to extract results.

Now that we have summed up the definitions of each of the subjects in discussion, is there something that you could infer? Yes? No?

Okay, let us help you with this. The statement that one can infer from this is that “Machine Learning is a part of Data Science”, right?

Not exactly.

In order to understand this better, check out the figure below.



 




 

For those of you who are wondering why machine learning is not shown as a subset of data science in the above figure, let us explain.

In order to understand where machine learning lies in the world of data science, we need to understand the whereabouts of artificial intelligence with respect to data science.

Artificial Intelligence can be defined as a discipline that helps induce the ability to learn, think, analyse and interpret into machines, hence its primary goal is to achieve machine intelligence. Data Science on the other hand has the sole objective of extracting useful information from data. 

From this we can imply we that the primary objectives of each of these fields differ. Hence Ai cannot be a subset of data science and it exists independently.

In a similar sense, we can analyse where machine learning lies. 

As machine learning is an approach that entirely supports artificial intelligence, the primary aim of Machine learning is to provide assistance in the development of artificial intelligence. As its primary aim is not information extraction, machine learning cannot be a subset of data science, but it is deemed as a subset of artificial intelligence.

But machine learning and artificial intelligence do provide assistance in making data science easier and simultaneously data plays a vital role in training and building artificially intelligent machines. In other words, the two fields complement one another.

This explains the intersection between the three sets in the figure above.

 

Towards the end, we can put out a recorrected statement that “Machine learning completely supports artificial intelligence and it also aids information extraction, in other words, data science.”

This statement clearly explains the above figure and also helps us clearly define where machine learning stands in the world of data science.

With the future heading towards the revolution of automation, a career in data science or machine learning has huge potential. But if we were to give you some inside information, one can highly accentuate their career prospects by being well informed and well knowledged in both, Data Science and Machine Learning.

On this note, we conclude this blog hoping that we have provided you with, at the least, a minimal clarity on this confusing comparison.

Cheers!

 

 

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Dedeepya Bypuneedi

A Computer Science graduate by education and a content writer by profession. Currently fulfilling her zeal to write by putting pen to paper every time she comes across something that is interesting enough to let the world know

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