Difference Between Big Data and Data Science
Big Data: It is huge, large or voluminous data, information or the relevant statistics acquired by the large organizations and ventures. Many software and data storage 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 uses 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 the huge information. It is about collection, processing, analyzing and utilizing of data into various operations. It is more conceptual. It is about extracting the vital and valuable information from huge amount of the data. It is a field of study just like the Computer Science, Applied Statistics or Applied Mathematics. It is a technique of tracking and discovering of trends of 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 mostly used in Big Data includes Hadoop, Spark, Flink, etc. Tools mainly used in Data Science includes SAS, R, Python, etc It is a super set 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 the 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.