Most people often mix the terms Data Science, AI, Machine Learning, and Data Mining or use some of them interchangeably. But all of these terms have their separate meaning and stream. Data has always been a central element of any business to make precise decisions from granular data. Even though all of these terminologies are somehow interrelated to data, each has its characteristics. This article will give you a clear understanding of all these four terminologies and their differentiation.

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What is Data Science?

Data science is an interdisciplinary domain that grows with different verticals allowing enterprises to extract and employ granular data for valuable insight. This field comprises data-handling processes through data science tools. This field of study integrates domain expertise, programming skills, and understanding of mathematics plus statistics to pull meaningful acuities from different datasets. Analysts extract these meaningful data to translate them into tangible business value. Data science practitioners apply data mining and machine learning to render actionable insights from numbers, text, images, video, audio, etc.

 

What is Artificial Intelligence?

Artificial Intelligence (AI) is a particular branch of computer science that simulates human intelligence, making it process through machines. Typical applications of AI are natural language processing, expert systems, speech recognition, image recognition, and machine vision. AI leverages trained data and machine learning models to make AI more efficient & make it seamless in mimicking human intelligence. Here both the terms data and ML have been used as all of them employ data to a certain extent.

 

What is Machine Learning?

Machine learning has become a buzzword, and almost every multi-national company is leveraging its power. Machine learning is an applied branch of AI that enables computing systems to understand and enhance intelligence to become better automatically from experience without being explicitly programmed. Through ML, computer science professionals focus on data and create algorithms for imitating human intelligence and learning. ML helps an AI system improve its accuracy gradually. You can notice that machine learning algorithms also leverage data for implementing their model.

 

What is Data Mining?

Data mining is another buzzword that has been around us for ages. It is the process of uncovering patterns from a massive amount of data. It is also known as knowledge discovery in data (KDD). Companies leverage these data to discover business intelligence that helps solve business problems, eliminate risks, and grab new opportunities. It is a branch of data science that extracts valuable information from a large pool of data and performs mining that ore data.

 

Data Science vs. Artificial Intelligence vs. Machine Learning vs. Data Mining – How do they differ?

Data has become the driving force for all modern technologies and business development. Pretty much every individual stumbles upon such terminologies at some point. But we have to be precise that data science is a discipline that leverages data and data mining techniques. Artificial Intelligence is a separate branch of computer science that simulates human intelligence through computing devices & machines. Machine learning (ML) is a branch of AI that helps AI systems improve their accuracy through self-learning. Researchers and ML professionals feed data to machine learning models to train them effectively to simulate human-like working abilities with more efficiency.

Here is a tabular representation of the differences between the above terminologies.

Data Science

Data mining

Artificial Intelligence

Machine Learning

It is a field or discipline that uses processes and programs to extract actionable insight from structured and semi-structured data.

Extract valuable information from large amounts of data through mining techniques.

AI is also a field that mimics human intelligence and leverages algorithms that work on their own intelligence.

Machine learning is a branch of AI that leverages data to extract knowledge based on past experience.

It contains datasets of structured, semi-structured, and unstructured data.

It contains a massive database of unstructured data.

It focuses more on the algorithm or programming logic rather than on data.

It teaches machines and computers to learn and comprehend from the different forms of data.

Branch that deals with data

Branch that deals with extraction and filtering of necessary data from unnecessary ones.

Branch that leverages precise programming logic on data

Branch that leverages data to become a self-learned and training system

Requires human effort to clean data, filter datasets that are of exact use, analyze data, and in visualizing them

Requires a lot of human effort

It requires less or no human effort as it can use its own intelligence

Requires no human effort once such models got designed, as they can learn and rectify on their own

Companies use data science for analyzing and visualizing data.

Companies use data mining for cluster analysis.

Companies use artificial intelligence to make smart computers and machines that can solve problems like a human.

Companies use machine learning to train machines to learn on their own.

Data analysts perform data gathering, cleansing, and analysis from mined data.

Professionals perform data mining from data warehouses.

AI developers write code, build logic, and leverage processed data to simulate human-like intelligence in machines.

ML developers work closely on AI program logic and data provided by data science professionals to train the models to learn through their experience.

Companies that frequently use data science in their projects are IBM, Wipro, Cloudera, MuSigma, etc.

Companies that frequently use data mining concepts are Amazon, Netflix, American Express, etc.

Companies that frequently use artificial intelligence techniques are Apple, Baidu, Amazon, Facebook, IBM, Microsoft, Tencent, etc.

Companies that frequently use machine learning techniques are Tesla, Apple, IBM, Microsoft, Intel, Baidu, Pinterest, etc.

 

As enterprises are getting inundated with tons of external and internal data, they need technologies and streams like data science, AI, ML, and data mining to distill raw data down to actionable acuities that bring fruitful meaning to business operations and decisions. ML and data science are enablers that can deliver faster and more accurate results for profitable outcomes at the least effort and risk.