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Data Science, ML and AI - What's the difference?

Updated: May 28


The Data Science industry is growing rapidly. Today, the world is very competitive. It is difficult to catch the race. We, at Skillenable Academy have tried to curate a composite course known as DEEP DIVE IN DATA SCIENCE-AI AND ML for you to help you choose the right path in the right way.


Let us begin with some simple questions….


What is Data Science?


Data Science was first introduced in 2009, when Google’s Chief economist and UC Berkeley professor realized the need and importance of understanding technology and reconfiguration of different industries.


In 1974 Peter Naur published a Concise Survey of Computer Methods in Sweden and the United States.

Peter Naur offers the following definition of data science: “The science of dealing with data, once they have been established, while the relation of the data to what they represent is delegated to other fields and sciences.”

The book is a survey of contemporary data processing methods that are used in a wide range of applications. The Preface to the book tells the reader that a course plan was presented at the IFIP Congress in 1968, titled “Datalogy", the science of data and of data processes and its place in education,“ and that in the text of the book, ”the term ‘data science’ has been used freely.”

In 1996 Members of the International Federation of Classification Societies (IFCS) met in Kobe, Japan, for their biennial conference. For the first time, the term “data science” is included in the title of the conference (“Data science, classification, and related methods”).(Source: Forbes: A very short history of Data Science).



In simple words, Data Science combined domain expertise and programming skills of mathematics so that one can extract meaningful and helpful information. It is the ability to understand, process, extract valuable information from the given data, visualize the same and draw results with respect to it.


What is AI(Artificial Intelligence)?


John McCarthy is recognized as one of the godfathers of AI. He defined AI as “the science and engineering of making intelligent machines”.

In other words, AI can be understood as a computer system which is able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.


What is ML(Machine Learning)?


Machine learning is a subset of AI.

All this might be a little confusing in the beginning , but we are surrounded by data science and it’s concepts all the time. From walking into a medical shop to liking posts on social media sites, all of the information is stored, analysed and as a result we get what we want with just one click.


To make it simple let’s look at some examples of Data science which we are unconsciously surrounded by :

  1. Most of us are very active on social media, be it Instagram, Facebook or any other site. We like, share and comment on images and videos.Right? But have you ever thought as to how your feed is filled with similar posts and links later as well? The answer to this interestingly simple question is DATA SCIENCE. Your activities are captured and then science is applied to it, this way you see similar posts of what you have liked before. For example if you have been liking posts related to online learning, you will be shown more posts with similar topics. This way you become a target customer.

  2. We all have a particular shop from which we buy are essentials, right? Have you ever noticed that during billing, the cashier asks us for our phone number...Wondered why? The answer is DATA SCIENCE. They register and keep a record of it so that the next time you purchase from the same shop, they know what you like and dislike. This helps them to send mails and sales messages of those products which you have shown interest towards.


We are always surrounded by some or the other calculations and data. Be it sitting in an UBER to watching Netflix at home.


Well, Data science is going to be the future soon. It is an ‘in-demand’ field which has supported a huge career opportunities at big and small companies.There are more that 4,500 open job offers listed on various websites. Certified Data scientists have tremendous opportunities to set a mark in the market, today.


According to Forbes :


  1. Jobs requiring machine learning skills are paying an average of $114,000. Advertised data scientist jobs pay an average of $105,000 and advertised data engineering jobs pay an average of $117,000.

  2. Annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020.

  3. 59% of all Data Science and Analytics (DSA) job demand is in Finance and Insurance, Professional Services, and IT.

Isn't that fascinating? Data science will soon shake the Indian market, job opportunities will grow further as a result the demand will also increase.


Well, with all this information handy, we are sure you would want to know more about the same.

Skillenable Academy has composed a detailed course on Data Analytics which will give you the right amount of information that you require to become an industry ready data analyst. The course, Deep Dive In Data Science-AI and Ml is a modern take of the traditional methods of calculating big data. This 24 weeks course will not only make you a certified data scientist but will also provide a minimum CTC of Rs 5.5 lakhs. Through this course you will get to work on real projects and face the world. This is a well structured course which consists of 7 data analytics tools.


Well, that is not it, stay tuned to know about each and every data analytics tool. For further queries feel free to leave a comment below!

Enabling a better tomorrow!





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