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Data Science vs Machine Learning and Artificial Intelligence

With the growing importance of data in every sector, terms like data science, artificial intelligence and machine learning have become new buzzwords. People often get confused and use them interchangeably which is wrong. These terms are interconnected but not interchangeable. There are some overlaps in the applications but each term has its own specific usage in the businesses. In this blog we will try to explain the meanings and applications of data science, machine learning and artificial intelligence. Whether you are from IT or non-IT background, you will have a clearer understanding of the terms by the end of the blog.

Data Science vs Machine Learning and Artificial Intelligence










Let’s have a quick look at these three terms before diving in the differences and uses.

What is Data Science?

Data science is used by the companies to get new results from the data. Data Science works with both structured as well as unstructured data. It involves every process related to data- data selecting, preparing, and analysing.

It uses various techniques involving statistics, mathematics, and programming to extract meaningful insights and patterns from the large volumes of raw data that are relevant for the businesses.

Among all three AI, ML and Data Science, data science is the most widely used technique. Data has become revenue in today’s world as more data can help the organization in generating more business insights. Companies are building recommendation engines using data science that can predict user behaviour. Accurate results can be achieved by applying various algorithms to the vast amount of data available. The more the data, more accurate the results that companies can get.

What is Machine Learning?

Machine learning is the science that aspires to enable computers to process new data input without explicit programming, based on the learned prototype. It aims at preparing machines to act and learn like humans, where the learning can be improved over the time in a self-sufficient way i.e., without feeding them manual categorical instructions to perform a specific task. In ML, data is fed to the standard algorithm, and the machine builds the logic of its own through that information. No code-writing is involved to perform tasks. In other words, ML is about making machines learn to program themselves.

ML helps in making programs scalable with better output results in shorter period of time. But the question that comes to most of us is: how do we make a machine learn? There are three different approaches used

1. Supervised

2. Unsupervised

3. Reinforcement learning

In supervised approach, machine uses labelled data to recognize the characteristics for future outputs. In unsupervised approach, unlabelled data is simply put and the machine understands and classify according the characteristics. For eg if we want to classify between birds and animals, supervised approach will provide some labelled pictures and then machine will classify the rest of the data while in unsupervised approach, data will be provided and the machine will classify it without any labelled data provided. In reinforcement approach, machine tries to learn algorithms by analysing the rewards and errors produced by the actions performed like the game of chess.

With the help of Machine learning, recommendation engines would not be possible as it is not realistically possible on a human level to go through millions of reviews, likes and search queries to determine the favourites of the customers.

What is Artificial Intelligence?

Artificial Intelligence can be referred to the imitation of human brain functions by machines. AI focusses on creating machines to do logical reasoning, learning, and self-correction like humans do. The apps that can play games to the speech recognizing devices like Alexa- all are related to AI. Devices are trained to solve problems and learn in a better way than humans can. It is accomplished with the help of artificial neural network created to replicate human intelligence. AI is one of the most complicated technology with lots of computing required. Artificial intelligence is categorised into two parts- General AI and Narrow AI.

AI can be described as a vast collection of algorithms(mathematical) to help device establish relationships between the bits of data provided in such a way that this information can be used to derive accurate decisions. Here it should be taken in account that data provided should be enough. If the data provided will not be enough then the accuracy can be low for the predictions provided. Artificial Intelligence needs data to learn and make accurate decisions, hence more the data available, more accurate the predictions/decisions provided will be. Also, the data provided should be accurate as AI focusses on self-correction.

 Data Science vs Machine Learning vs Artificial Intelligence


Data Science


       Machine Learning


Artificial Intelligence

Data Operations are included

Data Science collects, analyse and processes data to obtain information.

DS uses both structured as well as unstructured data.

Popular tools used are Apache and Tableau.

Healthcare Analysis is an example.

It is a subsection of Artificial Intelligence


ML feed data to the algorithms to perform explicit tasks without writing codes for the specific tasks.



Statistical models are used by Machine learning.


Popular tools used are Lex and ML Studio.



Recommendation systems like Spotify is an example.

It includes ML


AI uses vast amount of data through mathematical algorithms to make devices learn automatically.



Decision trees and logics are used by Artificial Intelligence.


Popular tools used are TensorFlow and Keras.



Chatbots is an example.

Data Science vs Machine Learning and Artificial Intelligence: Skills Required

Some of the required skills for the job as a data analyst, data scientist or data engineer that can help you stand ahead from the peers are data visualisation, data reporting, programming, risk analysis, and data warehousing.

Some of the skills required to grab the best jobs available in the market in ML and AI are programming language like c++ and python, ML algorithms, probability, Distributed computing, statistics and data evaluation.

From the skill set required, we can see that the requirements for both domains intersect. Data Science and ML and AI go hand in hand. Machine learning is possible only with data and data science can provide better results with ML. In the same way AI needs accurate data to make decisions and for self-correction.


Data Science vs Machine Learning and Artificial Intelligence: Career Scope

With more and more organisations showing increased interest in data, need for data scientists is increasing, who can understand the data available and can provide decisions for the business. Acc to a report, around 11 million jobs will be created in data science sector by the end of year 2026. Data scientist job is considered as one of the high paying jobs and hence can provide a lucrative career with attractive pay package.

If you are looking for Machine learning jobs in pune, or at any other location, you will find that the salaries are reasonably good. Demand for ML jobs is at all time high with increased demand for chatbots and smartphones. If you are interested in Machine learning, NOW is the time to enter the ever-growing sector for a great career.

According to a survey conducted by Indeed.com, AI is one of top roles in its list of job listings. AI has carved its way to the top of the list in a very small-time span, resulting in a demand-supply gap, making it a hot talent market right now. With the autonomous cars, facial recognition systems etc., AI is quickly becoming most demanding technology.


Data Science vs Machine Learning and Artificial Intelligence: Salaries

Skills you acquire is a big factor in determining the salary for any role. But apart from skills, there are many other factors that an employer takes into account for the salary. The common factors can be years of experience, location of the job, company hiring. In terms of experience, professionals with 1-4 years of exp are known as beginners, 5-8 years of experience are mid-level professionals while 8+ years of experience are known as experts in the respective field.

With the above classification in mind, we have collected some data on the salaries offered to data scientists as well as ML/AI professionals.


Experience Level


  Data Scientist


 ML/AI professionals




₹ 6,10,000 PA


₹ 5,00,000 PA




₹ 10,00,000 PA


₹ 6,80,000 PA




₹ 20,00,000 PA


₹ 20,00,000 PA

As we can see from the table above, salaries for all the jobs are absolutely awesome but the beginner’s salary for data scientist is a little higher than that of ML/AI engineers. However, with the experience, professionals of both the domains earn equal average salary of 20 LPA.


Data Science vs Machine Learning and Artificial Intelligence: Which is better for Career?

Data Science and machine learning and artificial intelligence are two different domains with distinctive branches of studies that can’t be compared. It is same as comparing science with commerce or arts. Data science is quite popular in recent market with the companies becoming more and more data hungry. Most of the companies aspire to make profitable decisions based on data research, which makes data science as a hot market for employers as well as employees. On the other hand, ML/AI is an emerging field with a lot of scope in the future. With the increased demand for artificial intelligence and machine learning, many companies are on the hiring spree and you can get the job according to your preference because of the demand-supply gap in the market. So, we can say that professionals from both domains have a bright future.


Are Data Science, Machine learning and Artificial Intelligence the same?

Well, the simple and plain answer is NO, they are not same. Data science is the study of the vast amount of data to make profitable decisions for an organisation. Data scientists use tools and techniques to derive useful patterns and information from the unstructured data to be used for the benefit of a company. While ML and AI emphasise on making devices capable of thinking and acting like humans. Machines are fed with the accurate data and with the help of algorithms, it will perform the task.

There are overlaps in these technologies. They all work better with each other but using these terms interchangeably is not correct.


Can Data Scientist become ML/AI Engineer?

Data Scientist can absolutely become a machine learning or artificial intelligence engineer. Machine learning is all about assisting the data science algorithms to work efficiently with larger data sets. ML engineers make sure that machines are ingesting the data properly. So if you are a data scientist who is dealing with those data sets on regular basis, that can give you a head start. There are many ML languages and libraries that are used in data science applications also. Of course, there will be a lot of hard work involved but this can be right path if you want to upskill and take new career opportunities.

Hopefully, this post will give you some clarity on the concepts of data science, machine learning and artificial intelligence. By now you must have made up your mind about the course you want to take and career path you want to pursue. If you need more guidance, EthansTech is providing courses on data science, machine learning and artificial intelligence. EthansTech is a reputed institute that is providing technical courses with the placement assistance to the students. If you want to register for the course or have any query, fill up our enquiry form or call us, our team of experts will be happy to help you.

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