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What is Application Programming Interface (API)

An application programming interface (API) is a set of programming interfaces that enables application developers to access and control software or a system. An API provides a structured interface between two separate software applications through which data, functions, and services can be exchanged. It provides a way to create an application without modifying the source code of the other application.

What is data preparation?

Data preparation is a process in which data is prepared for various purposes. In this process, the data is sorted, structured, analyzed and prepared in order to make it usable for specific applications. This can be achieved through various processes such as databases, data aggregation, data manipulation, data analysis and data visualization. Data preparation is an important part of data warehouse design and database management technologies.

What is an Analytical Model?

An analytical model is a mathematical or statistical approach used to analyze, understand, and predict complex phenomena. It is a mathematical model used to study a particular problem by examining different variables and properties of the problem. Analytical models are commonly used in science and engineering to study and understand various phenomena, and can also help make predictions about future trends.

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is a subfield of Artificial Intelligence that deals with natural language processing. It encompasses a range of techniques that attempt to translate human language into machine-readable formats and vice versa. Examples include automatic text analysis, machine translation, dialog systems, and text classification.

What is data enrichment?

Data enrichment is a process of enhancing and improving data by adding additional information to bring the data into a more structured or informed form. The purpose of data enrichment is to give users more insight into their data, allowing them to make better decisions and perform better analysis. Examples of data enrichment can include mapping geolocation data, demographic information, customer histories, or other external sources to existing data.