Data Science Vs Data Analytics

This Is Our Blog On Difference, Where You will be clear about Data Science and Data Analytics.

Data Science and Data Analytics are the buzzwords. Looking for long-term career potential, big data and data science jobs have long been a safe bet. This trend is likely to continue as AI and Machine Learning become highly integrated into our daily lives and economy. Today, data is the new oil for businesses to gather critical insights and improve business performance to grow in the market. Everything is done either by a data analyst or a data scientist.

Data Science and Data Analytics is a mishmash of terms that interweave and overlap with one another but are still quite different, they provide different results and pursue different approaches.

Data Science?

Data science is a multidisciplinary (use of mathematics, statics, computer science study & evaluate data) field focused on finding actionable insights from large sets of raw and structured data.

Data Scientist should have strong Business understanding, Math & Stat skills, Computer skills, Machine Learning skills, Communication skills.

You will Get to know more and details about Data Science in our previous blog Data Science — The Career Of Future.


Data analytics focuses on processing and performing statistical analysis of existing datasets and establishing the best way to present this data. Data Analyst, understand’s the data, analyzes the data, cleans the data, visualizes the information.

You will Get to know more and details about Data Science in our previous blog Breaking Into Data Analytics.

  • Data analytics focuses more on viewing the historical data in context while data science focuses more on machine learning and predictive modelling.

Difference Between Data Science & Data Analytics

Before coming to the differences and deep dive in difference study the table below-

Data science and Data analytics are unique fields, with the major difference being the scope. Data science is an umbrella term for a group of fields that are used to mine large datasets. Data analytics software is a more focused version of this. Another significant difference between the two fields is the question of exploration. Data science isn’t concerned with answering specific queries, instead of parsing through massive datasets in sometimes unstructured ways to expose insights. Data analysis works better when it is focused, having questions in mind that need answers based on existing data. More importantly, data science is more concerned about asking questions than finding specific answers.

The two fields can be considered different sides of the same coin, and their functions are highly interconnected. When thinking of these two disciplines, it’s important to forget about viewing them as data science vs data analytics. Instead, we should see them as parts of a whole that are vital to understanding not just the information we have, but how to better analyze and review it.

Sum up

This hierarchy diagram pretty much sums up the difference between data science and data analytics.

As you may have realized by now, Data science is vast and offers a more promising future. However, if you want to be closer to programming, Data analytics could be your best start. One thing is clear — both the fields are hungry for data and you need to work extensively with data to understand the whole picture. It is easier to move up the ladder from data analytics to data science.


Here We complete all our three Blogs on Data science, Data Analytics and the Difference between Data Science and Data Analytics.

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Author: Riya Patidar

Editor: Ashutosh Raghuwanshi, Devendra Patidar