OLAP

Data Science

An interdisciplinary field using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Big Data

Extremely large and complex datasets that require advanced computational tools and techniques to store, process, and analyse.

Data Engineering

The practice of designing, building, and maintaining data infrastructure, pipelines, and architectures.

Exploratory Data Analysis

An approach to analysing datasets to summarise their main characteristics, often using statistical graphics and visualisation.

Statistical Modelling

The process of applying statistical analysis to a dataset, identifying relationships and patterns within the data.

Diagnostic Analytics

Analysis techniques focused on understanding why something happened by examining data patterns and correlations.

Time Series Analysis

Statistical techniques for analysing time-ordered data points to identify trends, cycles, and forecasting patterns.

Regression Analysis

A set of statistical processes for estimating the relationships between dependent and independent variables.

Hypothesis Testing

A statistical method for making decisions about population parameters based on sample data evidence.

Bayesian Statistics

A statistical approach that incorporates prior knowledge and updates probability estimates as new data is observed.

Monte Carlo Simulation

A computational technique using repeated random sampling to obtain numerical results for problems with many coupled variables.

Business Analytics

The practice of iterative exploration of organisational data to drive business planning and decision-making.

Processing and analysing continuous data streams in real time to detect patterns and trigger responses.

Statistical models that predict the likelihood of a specific customer behaviour such as purchasing, churning, or responding to an offer, guiding targeted business actions.

The analysis of geographic and spatial data to discover patterns, relationships, and trends tied to location.

A formal agreement between data producers and consumers that defines the structure, semantics, quality standards, and service levels of a shared data interface.

The analysis of historical data to understand what has happened in the past and identify patterns.

Statistical and machine learning methods for predicting future values based on historical sequential data, applied to demand planning, financial forecasting, and resource allocation.

Using historical data, statistical algorithms, and machine learning to forecast future outcomes and trends.

Advanced analytics that recommends specific actions to achieve desired outcomes based on predictive analysis.

Business Intelligence

Technologies, practices, and strategies for collecting, integrating, and analysing business data to support decision-making.