Difference Between Data Science & Big Data


Data is everywhere and has become an essential part of our daily life in more ways than we ever imagined. The amount of data is exponentially growing, and as per Statista, the amount of data created in 2020 was 64.2 zettabytes and is expected to grow by 180 zettabytes by 2025. 


Data has become an important asset for all companies in the digital era. However, Big Data and Data Science are often misunderstood as these two are the hot topics across the data space. Hence, while working with all the data-related terminologies, it’s quite important to understand the different aspects and scope of it. Though people have started using Google to search for queries like ‘Best data science course in Pune with placement’ they should first be clear with the difference between data science and big data.  


<iframe width=”560″ height=”315″ src=”https://www.youtube.com/embed/rZscdOTs3s0″ title=”YouTube video player” frameborder=”0″ allow=”accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture” allowfullscreen></iframe>


This article will give you clear insight and help you understand the difference between Data Science & Big Data.


What is Data Science?


Data Science is a scientific approach that applies mathematical, statistical ideas, and computer tools for processing Big Data. Data Science is a subject that deals with structured and unstructured data; it’s a field that comprises everything related to data cleansing, mining, analysis, retrieving, and preparation.


What is Big Data?


Big Data is a collection of huge and complex data from different sources, growing exponentially with time. It is such large and complex data which is quite difficult to process and store using the traditional data processing system. 


Big Data has been classified into three types:


  • Structured – Transactional data, structured data formats, RDBMS, OLTP
  • Semi-structured – System logs, XML files, text files, etc
  • Unstructured – Digital content, tweets, emails, blogs


Structured data is easier to understand and extract than semi-structured and unstructured data. However, these data require some modification and customized modeling techniques to extract information from the data, which is usually done by the tools and other Data Science approaches.


Difference Between Big Data and Data Science


There may not be much of a difference between Big Data and Data Science, but these two terminologies have always instigated the minds of data enthusiasts and put them into a dilemma. 


Let’s move ahead and learn the major differences between these two terms, which will help you understand the core concepts and how they differ from each other.


  • To improve efficiency, understand the market, and enhance competitiveness Big Data is used by most of the organizations, while Data Science provides methods and modeling techniques to evaluate Big Data and extract information precisely.
  • The amount of data collected by companies can be huge and in different types, and that is why they pertain to Big Data, but to utilize the data to extract valuable information, Data Science is needed.
  • Big Data is a pool of raw data which has no value unless analyzed. In contrast, Data Science uses theoretical and practical approaches to dig information from Big Data. Thus, it plays an important role in utilizing the potential of Big Data.
  • Big Data focuses more on technology like Hadoop, Pig, Hive, and analytical tools, but Data Science focuses on strategies for business decisions.
  • Big Data analysis performs extracting information from a large amount of data set, which is also known as Data Mining, but Data Science uses machine learning algorithms to design and develop statistical models that can derive information from Big Data.
  • Big Data is characterized by 3Vs, i.e., Velocity, Variety, and Volume, but Data Science provides the technique to analyze and extract information from the data. 


Skill Required To Become a Data Scientist


  • In-depth knowledge and experience of R & SAS.
  • Well-versed in Python, Java, C, C++, Perl
  • Understanding of Hadoop platform, along with Hive and Pig
  • Write and execute complex queries in SQL


Skills Required To Become a Big Data Specialist


  • Analytical skills – Essential for determining which data is relevant while creating reports 
  • Statistical skills – Able to create new methods to gather, interpret and analyze a data strategy
  • Computer Science – Programming is the need of the hour
  • Business skills – Able to understand the business objectives and process


Salary in 2022


According to Indeed, the average salary of a Data Scientist is $75,167 per annum, and the average salary of a Big Data Engineer is $117,082 per annum. 


The current salary stats are for the USA, based on 3.1k salaries reported on Indeed. However, the salary varies from organization to organization, location, and the amount of experience and expertise you bring to the table.




The emerging field of Data Science and Big Data has been discussed and explored to an extent. However, with the rapid growth of data, it is fair enough to say that Big Data is going to stay in the coming years, and to analyze, extract, and identify the potential of the data, Data Science and its techniques are required. 


So, if you are looking forward to a career in the data space, it’s the right time to step in now, as you are well-versed in the difference between Big Data and Data Science.