While big data and data science are sometimes used interchangeably, they’re tangibly different concepts. Big data is a result or outcome of technological innovation and refers to extremely large sets of data, such as from sensors and smart devices that make up the Internet of Things. Data science refers to the process and technique of working with that data and the skills one develops to do so. Many fields intersect in data science, including statistics and computer sciences.

Big Data: Big data refers to expansive sets of data that are too large to be analyzed through traditional data-processing methods. Big data includes:

• Unstructured Data: This is data that has no identifiable structure or organization and does not fit with existing data models, rendering it incompatible with traditional databases or processing.
• Semi-Structured Data: This data has some structure but is not in a database. It often has metadata tags to help categorize group subsets of data, and it may have a hierarchical structure. These elements make the data easier to organize and interpret.
• Structured Data: Structured data is organized data, with a defined length, format, and data model, so it’s easiest to store, query, and analyze. It’s stored in the relational database (RDBMS) and is more accessible to algorithms or human-generated queries.

Data Science: Data science involves recording, storing, and analyzing massive amounts of data to gain valuable business insights – and developing new methods for doing so. You can become a data scientist as your primary occupation, or you can learn data science skillsets to add value to other occupations, such as cybersecurity or business management.

“What are big data and data science?” is a complex question, but knowing the answer may help guide your education and career decisions. This article will provide an in-depth breakdown of the differences between the two.

See the full article here: https://csweb.rice.edu/academics/graduate-programs/online-mds/blog/big-data-vs-data-science