What Is Difference Between Data Science And Big Data?

What Is Difference Between Data Science And Big Data? | Data Science | Emeritus

In a world where the collaboration of technology and humans is prevalent, increasing companies use data science and big data processes to automate different business activities like marketing, product prototyping, product design, formulating promotional strategies, interacting with customers, and others. Therefore, if you are planning to develop a career in automation and technology, it is imperative that you learn about data science, big data, and its intermediaries.

Here, in this article, we’ll focus on data science and big data- the two modern business concepts that deal with data and its interpretation. Let’s start with understanding how data science and big data are different from each other. And then delve deep into understanding data science and big data from the business perspective.

What Is The Difference Between Data Science And Big Data?

Data science and big data are two sides of the same coin. Companies looking to make the most of their data will apply big data techniques to handle their firm’s data. Meanwhile, data science is the process that harnesses the potential to turn big data into something that benefits the company. When planning to work around data for a lifetime, it is important to understand the scope and purpose of different terminologies. Therefore, to utilize data science and big data tools and techniques, you must know their difference. Read below to know how data science and big data are different.

1. Meaning
Big data is a data assortment technique. It groups and divides large amounts of data that are hard to store or save. These data cannot be managed with the traditional data analysis method. That’s why businesses employ different data science applications to deal with big data.
On the other hand, data science deals with data and draws actionable conclusions from it. In the process, the data is cleaned, assorted, segregated, and analyzed to help businesses or other fields in decision-making or to solve complex problems.

The answer to what is data science is incomplete without the inclusion of big data. Therefore, they both are interdependent on one another and so are an inevitable part of the business.

2. Utility
Companies are sitting over a mine of data, and by using big data techniques, the businesses can turn the data into something that will help enterprises solve a business problem, gain competitive advantage, develop business agility, and others. In this digital era, many firms are adopting big data significantly because it helps brands set realistic metrics and track return on investment (ROI) without peril.
Meanwhile, data science can be used in industries like retail, finance, healthcare, construction, transportation, and others to deploy different techniques to make smart and intelligent use of the data generated.

3. Application
Big data can be used in different sectors like gaming, healthcare, and travel. These sectors deploy big data techniques for using data to understand their audience and their requirements. For instance, big data can help medical organizations understand their patients’ clinical history and prescribe treatment accordingly.

On the other hand, data science is used in many business processes like online advertisements, internet searches, and other things. It is used in business processes to understand the customer, market, or competitor.

The application of big data and data science is wide and comprehensive and hard to define for many audiences. Therefore, to experience big data and data science applications, one must be an integral part of the business that uses their techniques and tools.
These key differences will help aspiring candidates distinguish between big data and data science. Therefore, the next part of the article will delve deep into understanding data science and big data. Also, understanding career growth in the field.

What Is Data Science And Its Application?

Many organizations use data science and analytics to make strategic decisions because they provide a deep insight into the challenges and opportunities. Therefore, many data science applications are widely used in businesses. Data science can be summarized as the process of data extraction and collection to draw meaningful insights.

Meanwhile, data analytics is the process of interpreting the data collecting. Data analysis, artificial intelligence, machine learning, and deep learning are some of the popular data science applications that got popular due to the advent of technology and social media. Besides the above-mentioned, there are many applications of data science that have come to be known and used by businesses.

What Is Big Data And Its Application?

Many use the terms big data, data science, and data analysis without knowing the exact meaning. Since we discussed what is data science in the preceding part of the article, here we will focus on understanding big data and its application in business.

Experts define big data as complex and large quantities of data sets. These data sets are so huge that the traditional data processing systems fail to manage them. There are three Vs of big data that help in understanding the basics of big data, they are:

  • Volume
  • Velocity
  • Variety

Big data has proliferated into a different sector. Many companies are applying big data systems to their business process. Similarly, data science also has made inroads into different fields. That’s why there are many employment opportunities for candidates trained in data science and big data systems.

However, knowing the basics of data science or big data is not enough to make a long and prosperous future in information technology. For that, you need to take up different certification courses, which prepare you for the job by providing deep knowledge about modern technological trends. Emeritus India has many certifications in data science and IT courses that help you develop a prosperous future.

About the Author

Senior Content Contributor, Emeritus Blog
Varun, a seasoned content creator with over 8 years of diverse experience, excels in crafting engaging content for various geographies and categories. Leveraging this expertise, he seamlessly translates complex concepts into enriching educational content for the EdTech domain. His keen understanding of research and life experiences helps him resonate with students and create fact-based content. He finds solace and inspiration in music, nurturing his creativity for content creation.
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