What is The Difference Between Data Science and Machine Learning?
One of the most common confusions arises among the modern technologies such as artificial intelligence, machine learning, big data, data science, deep learning and more. While they are all closely interconnected, each has a distinct purpose and functionality. Over the past few years, the popularity of these technologies has risen to such an extent that several companies have now woken up to their importance on massive levels and are increasingly looking to implement them for their business growth.
However, among aspirants, there seem to be clouds of misconceptions surrounding these various technologies. This post will help you get a clear picture of what the two diverse yet closely associated technologies are all about.
In simple words, data science is the processing and analysis of data that you generate for various insights that will serve a myriad of business purposes. For instance, when you have logged in on Amazon and browsing through a few products or categories, you are generating data. This data will be used by a data scientist at the backend to understand your behaviour and push you retargeted advertisements and deals to get you purchase what you browsed. This is one of the simplest implementations of data science and it keeps getting more complex in terms of concepts like cart abandonment and more.
- Data science involves the processes of
- Data extraction
- Data Cleansing
- And actionable Insights generation
A data scientist is responsible for being as inquisitive as possible with the data set in hand to make the weirdest of business connection. Tons of insights lie unnoticed in massive chunks of data and it is data science that sheds new light on areas like customer behaviour, operational shortcomings, supply-chain cycles, predictive analysis and more. Data science is crucial for companies to retain their customers and stay in the market.
For simple comprehension, understand that machine learning is part of data science. It draws aspects from statistics and algorithms to work on the data generated and extracted from multiple resources. What happens most often is data gets generated in massive volumes and it becomes totally tedious for a data scientist to work on it. That is when machine learning comes into action. Machine learning is the ability given to a system to learn and process data sets autonomously without human intervention. This is achieved through complex algorithms and techniques like regression, supervised clustering, naïve Bayes and more. One of the simplest applications of machine learning can be found on Netflix, where after you watch a couple of televisions series or movies, you could find the website recommending you shows and films based on your preferences, likes and interests.
To become a machine learning expert, you need to possess knowledge of statistics and probability, technical skills like programming languages and coding, data evaluation and modelling skills and more.
Data science is an all-encompassing term that includes aspects of machine learning for functionality. Machine learning is also part of artificial intelligence, where a distinct set of purpose is met on a whole new level. To understand the distinction better, a bit of a visual aid like this video would help.
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