Difference Between Data Science and Big Data


While Big Data and Data Science are both concerned with data, there are differences in the objectives and the field coverage of each respective domain. 

Data science is a broad term that refers to all of the approaches and instruments employed during the many stages of the usable data life cycle. Big data, on the other hand, usually refers to extraordinarily huge data sets that necessitate the use of specialized and frequently creative technologies and approaches in order to "use" the data effectively.

Data science is a common and well-known word that refers to a variety of data-related processes and approaches. Big data, on the other hand, is relatively new in the sense that the amount of data generated and the issues that come with it continue to necessitate the development of new and inventive technology and strategies to handle it.

Let us Define the two Domains Separately to Understand the Difference between Data Science and Big Data.

Data Science 

Data science is a broad term that comprises many different fields and abilities, but in general, it refers to all of the methods for extracting information and understanding from data.

Data is present in massive and exponentially increasing volumes everywhere. Regardless of the scale of the data being processed, data science as a whole represents the manner in which data is retrieved, conditioned, extracted, assembled, processed, analyzed, interpreted, modeled, displayed, reported on, and presented. 

Big Data

Big Data is simply a subset of data science wherein the data volumes are enormous and handling them poses logistical issues. The main challenge is gathering, storing, recovering, processing, and interpreting information from these massive data sets in an efficient manner. The algorithmic constraints make the collection and processing of these massive data collections difficult. As a result, specialized approaches and tools are necessary.

Big Data is the umbrella phrase for these massive data collections, specialized approaches, and tailored instruments. It's frequently used on huge data sets to better-balanced data analysis and discover trends, as well as to build prediction models.

Let us Consider a Few Points to Dilate the Difference Between Data Science and Big Data

Domain Difference between big data and data science: Data Science is relatively a discipline. Big Data is a method for gathering, storing, and processing large amounts of data.

Operative Difference: 

Data science is the process of gathering, processing, analyzing, and incorporating data into various functions. It's more abstract.

On the contrary, Big data is the process of obtaining important and valuable insights from massive amounts of data.

Technique: Data science, like Computer Science, Applied Statistics, and Applied Mathematics, is a subject of study. Big Data is a strategy for detecting and tracking trends in large data collections.

Objective: The purpose of data science is to create data-centric products for a company. Big Data's purpose is to make data more crucial and usable, i.e. by extracting key significant information from large amounts of data while maintaining conventional elements.

Tools Used: Big Data tools include Hadoop, Spark, Flink, and others. While  SAS, R, Python, and others are commonly used tools in Data Science.

Domain Coverage: Data science encompasses data scraping, cleaning, visualization, statistics, and many other approaches, making it a superset of Big Data. Big Data is a subset of Data Science that includes mining activities that are part of the Data Science pipeline.

Application: DS is primarily utilized in the scientific community. Big Data is primarily used in the corporate world to improve consumer happiness.

Concept: DS is a broad term that refers to the science of data. Big Data is mainly concerned with the processes of dealing with large amounts of data.

Formation: Internet users, traffic/live feeds, and data collected from system records are all sources of science. Data filtering, processing, and analysis are used to create big data.


Application of Big Data and Data Science

To get a clearer comprehension of how big data and data science are different from one another, let us discover the applications of both the field in our daily lives

Data Science Applications

Internet Searches: Data science techniques are used by search engines to give the best suggestions for web searches in seconds.

Advertisements in Digital Platform

From display billboards to digital billboards, data science techniques are used throughout the digital marketing spectrum. This is the primary explanation for the increased click-through rates of digital ads compared to traditional ads.

System of Recommendation

Not only do recommender systems make it simple to identify suitable products from the sea of options, but they also improve the user experience. Many businesses utilize this technique to advertise their products and recommendations based on the needs of the user and the information's relevance. 

Big Data Applications

Financial Services and Big Data

Big data is used by credit card companies, retail banks, private wealth management advisories, insurance companies, venture capital firms, and institutional investment banks. The huge amounts of multi-structured data that exist in various separate systems, that big data can solve, is a shared challenge among them all. 

Communications and Big Data 

Telecommunication service companies' primary priorities include acquiring subscribers, customer retention, and increasing their current subscriber bases. The ability to integrate and evaluate the massive amounts of customer-produced and Machine-derived data that are generated every day holds the key to solving these problems.

Big Data in the Retail Industry

Whether an in-store or an online retailer, the key to staying competitive is to better understand your customers. This necessitates the capacity to examine all of the different data sources that businesses deal with daily.

That was all about the difference between data science and big data. Both professions are seeing a significant increase in demand for qualified practitioners, and they are swiftly becoming some of the most popular and lucrative careers.

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