Descriptive analysis is the analysis of historical data to determine what is, what has changed, and what patterns can be identified.
Considered the most basic type of analysis, descriptive analysis involves breaking down big data into small pieces of usable information, so that companies can understand what is happening in a given business, process or set of operations. Descriptive analytics can provide insight into current customer behaviors and business trends to support decisions about resource allocations, process improvements, and overall performance management. Most industry observers believe that descriptive analysis represents a large part of the logical analysis used by companies today.
Data intensive refers to data structures in which most cells or fields in the data matrix or data set are filled, with minimal empty or zero values.
Feature Engineering is one of the most labor-intensive and creative phases of the machine learning process. This process involves the transformation of raw data into more meaningful and processable properties. The basic principles of Feature Engineering include using domain knowledge, data discovery, understanding the nature of data, and problem-oriented thinking.
Business intelligence (BI) is the process and methods that enable organizations to generate more meaningful results and make data-driven decisions using tools such as data mining, data visualization, business analytics on existing data so that they can make better decisions.
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