Education

Data Science v/s Machine Learning – Things You Should Know

Data Science and machine learning are the “in” topics of today’s tech world. With the considerable amount of data being generated and machines becoming smarter and more intelligent, both data science and machine learning have become important niches in the artificial intelligence domain.

What is Data Science?

Data science aims to use a scientific approach to handle vast amounts of data (both structured and unstructured) and analyse it to extract useful information out of it. Here data is cleaned, filtered, stored and analysed to find useful details in the forms of trends, patterns, etc. by using analytical, programming and business skills on the data. Techniques such as linear regression, density estimation, and confidence interval are usually used on the data set. To learn data science, one must also re-visit basic statistics studied during school days.

What is Machine Learning?

Machine learning is the scientific study and technique of creating algorithms and statistical models that can learn from their environment and past data sets to become smarter and make better decisions. It relies on patterns, inferences, and past data sets to predict the correct match or answer. Machine learning focusses on programs that can learn from itself. Techniques such as regression, classification, and clustering are usually used on the data set. 

Data Science v/s Machine Learning? What’s the difference?

A data scientist is usually responsible for the collection, analysis and interpretation of vast amounts of data to find out data-based analysis, trends and patterns. A machine learning engineer, on the other hand, is responsible for creating and developing various models which aim to provide an optimum solution for the problem to be solved.

The role of data science is to define new problems which can be solved using machine learning or various other manual statistical model and machine-learning would be used to solve it to get the most optimum solution. For example,in order to find out an optimum path between many cities, data science is used to collect the data of all cities, their distances, all possible routes and options available among others. These can then be analysed to find all the feasible possible routes along with distances between each city. Then machine learning algorithms would be used to find the shortest path or the most optimal solution to the problem.

While both are a part of artificial intelligence, both are different in what they aim to achieve from the data, or the information provided. Machine learning is dependent on data science to provide the initial data set, which is used to train and test the algorithm or the learning model. Data science works on raw data sets to prepare to generate cleaner and more useful data sets for the algorithms. These machine learning algorithms then work with these refined data sets to train and learn how to make better decisions in any situation.

For example,on the user data collected by Facebook, data science would be used to find which user or person likes which type of content by analysing their usage pattern and demographics. Machine learning will use this analysis to recommend interests to other users who have similar usage history.

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