A/B testing is a method of comparing two versions of something, such as a website page, an app feature, or a marketing email, by showing each version to a different, randomly assigned group of people and measuring which version performs better against a specific, predefined goal. Named simply for comparing a baseline version A against
A method of comparing two versions of something by showing each to a randomly assigned group and measuring which performs better.
Big data refers to datasets so large, fast-moving, or structurally complex that traditional data processing tools and methods cannot practically capture, store, manage, or analyze them. The term is often explained through several defining characteristics, commonly called the dimensions of big data, including volume, the sheer size of the data involved, often reaching far into
Datasets so large, fast-moving, or complex that traditional tools cannot practically capture, store, or analyze them.
Cluster analysis is a data analysis technique that groups a set of objects or observations into clusters, so that items within the same cluster are more similar to one another than they are to items in other clusters, based purely on patterns found in the data rather than any predefined categories decided in advance. Unlike
A data analysis technique that groups similar objects together into clusters based on patterns in the data, without predefined categories.
Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset before it is used for analysis, reporting, or training a machine learning model. Raw data collected from real-world sources, including manual entry forms, sensors, and combined records from multiple systems, is rarely perfectly accurate or consistently formatted,
The process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset before analysis.
Data mining is the process of analyzing large datasets to discover previously unknown patterns, relationships, and useful information that would not be obvious from simply looking at the raw data directly. The term borrows its imagery from physical mining, since both processes involve sifting through a large quantity of raw material to extract something valuable
The process of analyzing large datasets to discover previously unknown, useful patterns and relationships.
In simple terms Data visualization is the practice of turning numbers and data points into charts, graphs, maps, and other visual forms, so that patterns and comparisons are easier to see than they would be in a table of raw numbers. [1] A chart earns its place by making a pattern visible, not by decorating
Data visualization is the practice of representing data as charts, graphs, and maps so that patterns and comparisons are easier to see than in raw numbers.
A data warehouse is a large, centralized repository designed to store integrated data gathered from many different source systems across an organization, structured specifically to support analysis and reporting rather than the everyday transaction processing those source systems were originally built for. Where an individual sales system, inventory system, and customer service system each store
A large, centralized repository that integrates data from many source systems, structured to support analysis and reporting.
Predictive analytics is the use of historical data, statistical techniques, and machine learning methods to make informed estimates about future events or outcomes that have not yet happened. Rather than only describing what has already occurred, which is the role of simpler descriptive analytics, predictive analytics builds models trained on patterns found in past data
The use of historical data and statistical or machine learning techniques to estimate future events or outcomes.
Regression analysis is a statistical technique used to understand and quantify the relationship between a variable of interest, called the dependent variable, and one or more other variables, called independent variables, that may help explain or predict it. A real estate analyst might use regression analysis to understand how a house’s size, location, and age
A statistical technique used to understand and quantify the relationship between a dependent variable and one or more independent variables.