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The difference between Data Science and Data Analytics you need to know.

You are currently viewing The difference between Data Science and Data Analytics you need to know.

“There were 5 exabytes of information created between the dawn of civilisation through 2003, but that much information is now created every 2 days.”

Last Updated on: August 1, 2022

Difference between Data Science and Data Analytics

The main similarity between data science and data analytics is that both offer great scope of career advancements. Often people use these terms interchangeably. Although they sound similar, data science and data analytics are distinct. Data science encompasses different fields related to mining large datasets. Data analytics is a part of data science that is focused on extracting actionable insights for applying to existing queries. 

To understand the difference between data science and data analytics, consider the realm of exploration. Data science merely parses through massive datasets, in a structured or unstructured way, without focusing on answering specific queries. The more random parsing exposed insights, some of which may have not been intended. On the other hand, data analytics focuses on specific queries and analyses the existing data to answer them. 

Both data science and data analytics use big data. Data science establishes trends in the data and can identify the most important questions to ask. Data analytics helps answer these questions. 

Data Science

Data Analytics

ScopeMacroMicro
ObjectiveAsking the right questions Finding actionable insights
Major AreasCorporate analytics, chatbots and search engine engineeringGaming, healthcare, and travel

While data science and data analytics are distinct, they do have a deep correlation. When used together, they can produce powerful insights across industries. In fact, they can be thought of as the two sides of the same coin. Data science parses big data to identify trends, highlight observations and suggest future insights. This information becomes an input for AI (artificial intelligence) algorithms, helping them understand the information and continue learning. However, data science can highlight various questions, when highly unexpected ones. Once this is done, the unknowns can be converted into actionable insights by using data analytics.

To clear your concepts and understating of the fields, consider GeekLurn’s Data Science Architect Program. It can equip you with tech-enabled, job-relevant skills. You will learn about the design and deployment of Big Data to convert it into real-time applications as well as Analysis Modules, Hadoop Development, Testing and Statistical computing. You even get to work on real-time case studies and gain project experience and certifications. It comes with 100% moneyback guarantee and offers 100% job placement.

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Monica is a senior marketing executive. Her skillsets consist of digital marketing and strategy, SEO, marketing analysis and more. She also has her expertise in writing various copies, including web, newsletters, e-books, social media, etc. But, it does not stop here. Her love for writing goes as far as doing poetry connecting science and life.

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Monica Swain

Monica is a senior marketing executive. Her skillsets consist of digital marketing and strategy, SEO, marketing analysis and more. She also has her expertise in writing various copies, including web, newsletters, e-books, social media, etc. But, it does not stop here. Her love for writing goes as far as doing poetry connecting science and life.
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