Synthetic Data for Analytics

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.

Changes in the underlying patterns that a model was trained to capture, requiring model adaptation.

A metadata management tool that helps organisations find, understand, and manage their data assets.

Identifying data points that differ significantly from other observations in a dataset.

The measure of data's fitness for its intended purpose based on accuracy, completeness, consistency, and timeliness.

A behavioural analytics technique that groups users with shared characteristics to track metrics over time.

The practice of building narratives around data insights using visualisations and narrative techniques.

Changes in the statistical properties of data over time that can degrade machine learning model performance.

An isolated repository of data controlled by one department, inaccessible to other parts of the organisation.