Data Science vs Machine learning (the buzz words of the decade)

van diagram of data science and machine learning
Reading time: 3 min

The biggest confusion that comes to mind while reading about the next major technological trend is what is difference between the major buzz words like machine learning and data science.

When they actually sounds like the same – difference between these is divided by just small line

Before going ahead lets have a look to google trend graph here form 2012 to 2018



the interest for machine learning is continuously growing and people are eager to learn this beautiful field .

So the billion dollar question what they are and what is the difference?

Data Science — Data Science is the discovery of data insights ,its also about asking the right questions

What does it mean??

Data science is the extraction of relevant insights from data. Its the process of understanding the data ,visualizing ,understanding what are the signals data is giving .

To give you an example of the same —

If a shopkeeper is keeping all the records of its customers in a diary ,like (who is the buyer ,how much he is buying and what are the most used products )).. He is collecting the data (the unprocessed one )

Why not if we can process that data and make it a information and perform some sort of analysis to it . That processing of data and getting the answer to some of the important questions –like

  • On what day their was the high sale?
  • Which is the highest selling product?
  • What do the people think of the shop?
  • What is the quality of user experience am providing?

These types of question will much more easy to answer with data science practices

Some of the data science practices are

  • Data visualization
  • Data extraction
  • Data cleaning
  • Data mining
  • Statically analysis

Machine learning — machine learning is a making computer learn from the data set provided ,gather insights and make predictions using algorithms 

machine learning can be performed using multiple approaches

  • supervised
  • unsupervised
  • reinforcement learning.

for example — on account of the above example of a shopkeeper some of the way he can implement machine learning practices in his shop ,could be by training a bot that will make sure to check the expiry of all the products and inform the owner that how much of the product is expired or which is about to expire in the coming days 

It Sound cool right !! 

Let me give you a live working of this same implementation of amazon implementing in its “GO STORES”


Conclusion —

Data science is the bigger umbrella which includes machine learning practices (some of them )

It uses various techniques from many fields like mathematics, machine learning, computer programming, statistical modeling, data engineering and visualization

In the case of machine learning its process of training the model (device ) to predict something from the clean data set which is provided by the data science practices

For machine learning we need clean data set (the data which is not cluttered ) to train the model on


Thanks !!



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