What are the Differences between Data Science and Big Data?
Let us talk about What Data Science actually is?
According to Wikipedia Data Science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, algorithms, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.
Data Science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing and aligning the data.
Let us talk about What Big Data actually is?
Big Data refers to humongous volumes of data that cannot be processed effectively with the traditional applications that exist. The processing of Big Data begins with the raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer. Big Data is something that can be used to analyze insights which can lead to better decisions and strategic business moves.
The definition of Big Data, given by Gartner is, “Big data is high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation”.
Applications of Data Science:
Recommender systems: The recommender systems not only make it easy to find relevant products from billions of products available but also adds a lot to user-experience. A lot of companies use this system to promote their products and suggestions in accordance with the user’s demands and relevance of information. The recommendations are based on the user’s previous search results.
Internet search: Search engines make use of data science algorithms to deliver best results for search queries in a fraction of seconds.