The  amount and definition of data has been increasing and expanding day by day. Earlier data storage was a problem, whereas now, the focus has shifted to processing of the huge amounts of data. Given the difficulty in inferring the data and drawing conclusions out of it using traditional ways of analyzing, Data Science has emerged as a solution to the problem. This problem of sifting through large amounts of data and drawing patterns out of it has led to the path of data science which in turn is a fusion of statistics and software engineering.

Application of data science

Imagine a world where we had satellites, radars, cameras which registered lots of information; but there was a lack of any system with the ability to analyse this information; no such system which could process the data , no such system which could produce the desired output. Don’t you think all our inventions would be in vain if we didn’t have a suitable system which could analyse it? What would be the use of all the registered data if we did nothing from it?

Therefore, we need to thank data science for coming to the rescue and providing us ways and techniques to analyse and draw meaningful conclusions from the data. Data science is a big tree with several branches dealing with the core activities needed for analysing information.

Some examples include: Internet Search, Digital Advertisements, Profiling customers, Recommendation systems on Netflix or Amazon Prime, drone cameras, Google Maps, self-driving cars, image recognition, Airline route planning, Price comparison websites etc. The list is ongoing. All these inventions or applications have been possible because of data science.

Data Science: Meaning and Uses:

Data science is a very broad concept, unspecific yet aspiring. It uses various forms of scientific methods, algorithm systems to analysis data and draw inferences out of it. It’s all about discovering patterns in large sets of information. Data science is like an ocean; you have to dive into it to understand its depth, to understand how helpful it is to a firm. It has various aspects in it, namely machine learning, data visualization, algorithm designing, distributed computing, web developing etc.

A professional dealing with data science is a data scientist, who must be in the position of explaining business implications of the analysed results better than any scientist. He must have hacking abilities, subject matter expertise and statistical skills in order to analyse,  propose and validate the information collected.

Data science as a predictive and prescriptive tool

Data science not only helps you to comprehend  data but also helps you to predict the near future. In today’s age, there is a lot of talk about cookies which are used to understand consumer needs. We have also come across various technologies which have sensors, lasers, cameras  etc. that not only provide a road map but also tell you the amount of traffic in a particular area, the estimated time it would take to reach your destination, any alternate routes to the destination, if your destination would be open or closed at the time you reach there, etc. Such amazing analysis has been possible because of data science. It helps businesses in understanding their consumer better and thus, taking suitable decisions based on predicted outcomes.

Data science is a type of predictive and prescriptive tool. It studies the past and present data to predict and prescribe suitable action in the future. Share brokers and various other agents use this tool which studies the past and present of a company and makes prediction about the future position of the company.

Machine learning – The Ability To Self-Learn

Machine learning is an outcome of data science. It is a branch of data science. It has stemmed out of data science. Machine learning would not have been possible without Data Science. It refers to the machine’s ability to function as a human, who learns from its experiences. Machine learning is an application of data science such that the machine itself collects data, draws conclusions from it and prescribes future actions without any human interaction or instruction. It is a self-evaluating and self-learning tool. In simple words, machine learning can be explained as an application of data science which allows the computer to learn on its own without any human assistance.

Machine learning uses various types of algorithms depending on the type of the problem. Where on the one hand, we earlier had to analyse the entire data, go through it and draw inferences sometimes based on experience and sometimes based on intuitions, but with the advancement of technology, our system could act like an AI which could make predictions and which could prescribe us with an effective solution. This is the wonder of machine learning.

Applications of machine learning:

Let’s take an example of a search you did on the internet this morning. For example: you searched about latest movies playing near you. Now, you move on to play some games on the phone where you see pop up advertisements, and to your surprise, this advertisement is of movies playing near you, or theatres near you or a movie ticket booking application etc.

Have you ever wondered how is it that the game finds out what you are looking for?

It’s all made possible with machine learning which tracks the consumer’s searches, analyses the searches to look for desires, the user’s interests and displays related advertisements. It is beneficial for both the consumer as well as for  the company as the details of the product have come to the person without any efforts and the company has benefitted from an increase in sale.

Another application of machine learning is the predictive tool or autocorrect tool on messaging applications. These tools automatically correct a misspelled word or predict the next word you’re going to type before you type it. This is all possible because the machine has learnt your typing patterns and recognised what you type after what word. Therefore, it gives suggestions as to your next word. This is another marvellous application of machine learning and data science.

Machine learning has also extended its scope towards the health sector where it can be used to diagnose a disease and prepare a report based on the disease.


The scope of data science is too wide and has plenty of opportunities for one to grow in this field. The applications and possibilities of data science are expanding each day with machine learning forming a part of every new technology that comes in the market. Therefore, these emerging technologies require talented personnel and professionals to contribute to the growth of the subject. A bit of hard work and dedication will definitely provide you with the expertise in the field of data science and machine learning. Therefore, a career in data science is the next big thing and you must enroll today and tap the potential of the subject!


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