Data Science: Dealing with unstructured and structured data, Data Science is a field that comprises of everything that related to data cleansing, preparation, and analysis.
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.
In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data.
Big Data: 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.
A buzzword that is used to describe immense volumes of data, both unstructured and structured, Big Data inundates a business on a day-to-day basis. 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”
Data Analytics: Data Analytics the science of examining raw data with the purpose of drawing conclusions about that information.
Data Analytics involves applying an algorithmic or mechanical process to derive insights. For example, running through a number of data sets to look for meaningful correlations between each other.
It is used in a number of industries to allow the organizations and companies to make better decisions as well as verify and disprove existing theories or models.
The focus of Data Analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.