Significant Difference Between Data Science and Big Data Course

Posted by Cyfinity Global on January 29th, 2021


Difference Between Data Science and Big Data Course 

Everything in the world is a composition of data. Companies and corporations dealing with sales, marketing, software development, and so on acquire large chunks of data from their customers. The companies must manage this data to ensure information security. In this domain, Big Data is a weapon to solve data managing having various technologies, including machine learning, artificial intelligence, and others.

Most people find themselves confused between Big Data and Data Science courses. So many questions encircle them, including 

  • Which is better?

  • What technology is in demand?

  • What are their applications?

So, to solve your query in this blog, get a better insight into the differences between the two.

  • Difference in Concept

Big Data:

Big Data is a structure of a large volume of data that is hard to handle with traditional tools and technologies. It is hard to work with raw data and store it on a single machine. Various technologies, including machine learning, and analytics, data processing takes place. In this way, companies handle large volumes of data.

Data Science:

Data Science is a way of settling data in various steps starting from data wrangling, preprocessing, visualization, and then performing data modeling to get requisite results that everyone can understand.

  • The difference in Skills to Pursue Each Course

Big Data:

If you are willing to start with Big Data courses, you need to work on the following skillset:

  • Analytical Skills: It is a necessary skill in Big Data courses. It checks your capability of working and understanding data. It is how good you are at understanding, analyzing, and manipulating data as per your need.

  • Business Skills: A business understanding or business skills are a must in Big Data courses. These skills help you build your communication skills, leadership skills, and management skills.

Data Science:

  • Knowledge of Databases: It is necessary to understand databases and SQL queries to work with large datasets. In Data Science courses, you will require various tables and database queries dealing with various relational operations on tables.

  • Programming Language: If you choose for data science courses, knowing a programming language is a prerequisite. The most used languages are Python and R programming languages.

  • The difference in Applications After Pursuing Each Course

Big Data Courses:

  • Finances: Big data courses are helpful in financial servicing industries. These industries include banking services, the retail market, insurance firms, and others. 

  • Retail: Every domain needs customer understanding. So, be it any online retailer, big data is a tool to understand customer needs to be based on past experiences via analytics. 

  • Communication: Another usage of Big Data courses is in telecommunication. According to subscribers, analytics is necessary to judge what future needs will demand.

Data Science Courses:

  • Recommendation Systems: One of the most common applications of data science courses is the recommendation system. In day-to-day life, you might come across various recommendation systems like the youtube platform, Facebook showing your recent Amazon searches, and many more.

  • Internet Research: Internet research or search is another application of Data Science courses. Search engines depend on data science to get query search results. 

  • Advertisements: Data science courses, including data analysis and data visualizations, can help you land jobs related to marketing strategy. Marketing and sales company uses data science knowledge in managing their digital advertisements.

Bottom Line

It appears both Data Science and Big Data courses are closely related, and this is true. Although there are different skill sets for both, professionals in both domains work closely and in teams.

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Cyfinity Global
Joined: November 19th, 2020
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