Data analysis

information assets characterized by such a high volume, velocity, and variety to require specific technology and analytical methods for its transformation into value

field of study to extract insights from data

activity for gaining insight from data

NoSQL (originally meaning "Not only SQL" or "non-relational") refers to a type of database design that stores and retrieves data differently from the traditional table-based structure of relational databases. Unlike relational databases, which organize data into rows and columns like a spreadsheet, NoSQL databases use a single data structure—such as key–value pairs, wide columns, graphs, or documents—to hold information. Since this non-relational design does not require a fixed schema, it scales easily to manage large, often unstructured datasets. NoSQL systems are sometimes called "Not only S

Netvibes is a French brand of Dassault Systèmes that previously ran a web service offering a dashboard and feed reader. Currently, the company offers business intelligence tools.

summarization tool

measure of similarity between vectors of an inner product space

process that creates features for machine learning by transforming or combining existing features

computer program designed for storing, retrieving and managing semi-structured, document-oriented information

data storage paradigm designed for storing, retrieving, and managing associative arrays

common method of quality control, data acquisition, and data analysis

data mining process model

data missing or collected but not analysed

integration of multiple data sources to provide better information

analysis of datasets using techniques from topology

concept in economic inflation

DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and informati

process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data

technique for solving the problems that are caused by making causal inferences on the basis of only a small number of cases

statistical analyses that were not specified before the data were seen

information delivered immediately after collection

change of statistical properties over time

database management system designed to support multiple data models against a single, integrated backend