What is the difference between Data Science and Big Data?

Data Science, AI and ML
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#1
Big Data refers to the humongous amount of data which is tracked by businesses on a day to day basis. This data is majorly unstructured; and is generated by routing business processes running on a daily basis. The nature of Big Data in organizations is defined by 3Vs – Volume, Velocity & Variety. ‘Volume’ refers to the large quantum of data in terabytes which may be transactional data, customer data etc. ‘Velocity’ refers to the rate at which the data is generated which could be either scheduled batch process, or real-time data collection. Finally, ‘variety’ denotes the variation in the data formats and structures.

Data science, on the other hand, is a more specialized field which involves using mathematical & statistical modelling techniques on underlying data to devise patterns in data; and find actionable solutions to real-world problems.

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#2

Big Data : is a concept which talks about how to manage data . The basic principles are Velocity , Volume , Variety . It’s all about collecting , managing , processing data.

Data Scientist : Is a role or job who is expected to work on big or small data to gain sights out of the data . He is person who is responsible for writing algorithms do learn things on it’s own . It’s one of the job description .

#3

Some important distinctions between big data and data science concepts are as follows:

  • Big data is required by businesses to improve efficiencies, get a better understanding of new markets, and raise competitiveness, whereas data science provides the methods and procedures for efficiently comprehending and using big data’s promise.
  • Currently, there is no limit to the amount of relevant data that businesses may collect; however, data science is required to extract meaningful information from all of this data for organizational decisions.
  • Data science is characterized by the methodologies or procedures used to evaluate data specified by the 3Vs, whereas big data is defined by its velocity, variety, and volume (often referred to as the 3Vs).
  • Big data has the potential to boost productivity. Extracting insight information from big data in order to optimize its performance-enhancing potential, on the other hand, is a significant challenge. Data science employs theoretical and experimental approaches in addition to deductive and inductive reasoning. It is in charge of unearthing all buried useful data from a complex mesh of unstructured data, supporting businesses in realizing the promise of big data.
  • The technique of obtaining useful information from large amounts of data is known as big data analysis. Data science, in contrast to analysis, uses machine learning techniques and statistical methodologies to train a computer to learn without requiring considerable programming in order to make predictions from massive volumes of data. As a result, big data analytics and data science should not be confused.
  • Technology (Hadoop, Java, Hive, and so on), distributed computing, and analytics applications and tools are all part of big data. Data science, on the other hand, focuses on business decision-making strategies and data dissemination using mathematics, statistics, and the previously mentioned data structures and processes.

Given the distinctions between big data and data science outlined above, it’s important to note that big data encompasses data science. Data science is important in a wide range of applications. In data science, big data is used to gain valuable insights through predictive analysis, which may subsequently be used to make informed decisions. As a result, data science is incorporated into big data rather than the other way around.