Difference Between Big Data and Data Science

Big Data: It is huge, large, or voluminous data, information, or the relevant statistics acquired by large organizations and ventures. Many software and data storages is created and prepared as it is difficult to compute the big data manually. It is used to discover patterns and trends and make decisions related to human behavior and interaction technology. 

Data Science: Data Science is a field or domain which includes and involves working with a huge amount of data and using it for building predictive, prescriptive, and prescriptive analytical models. It’s about digging, capturing, (building the model) analyzing(validating the model), and utilizing the data(deploying the best model). It is an intersection of Data and computing. It is a blend of the field of Computer Science, Business, and Statistics together. 

Below is a table of differences between Big Data and Data Science:

Data Science	Big Data
Data Science is an area.	Big Data is a technique to collect, maintain and process huge information.
It is about the collection, processing, analyzing, and utilizing of data in various operations. It is more conceptual.	It is about extracting vital and valuable information from a huge amount of data.
It is a field of study just like Computer Science, Applied Statistics, or Applied Mathematics.	It is a technique for tracking and discovering trends in complex data sets.
The goal is to build data-dominant products for a venture.	The goal is to make data more vital and usable i.e. by extracting only important information from the huge data within existing traditional aspects.
Tools mainly used in Data Science include SAS, R, Python, etc	Tools mostly used in Big Data include Hadoop, Spark, Flink, etc.
It is a superset of Big Data as data science consists of Data scrapping, cleaning, visualization, statistics, and many more techniques.	It is a sub-set of Data Science as mining activities which is in a pipeline of Data science.
It is mainly used for scientific purposes.	It is mainly used for business purposes and customer satisfaction.
It broadly focuses on the science of the data.	It is more involved with the processes of handling voluminous data.

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