Data Analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information.
Data analytics is used in many industries to allow companies and organizations to make better business decisions and in the sciences to verify or disprove existing models or theories. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.
DA relies on the simultaneous application of statistics, computer programming, and operations to quantify performance. Organizations may apply analytics to business data to describe, predict, and improve business performance.
Data Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns to make effective decisions.
Areas within data analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, supply chain analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics.
Since analytics can require extensive computation, the algorithms, and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
There is increasing use of the term advanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks, Decision Tree, Logistic Regression, linear to multiple regression analysis, classification to do predictive modeling. It also includes “u