Market Basket Analysis

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.

A technique for determining which input variables have the most significant impact on model predictions.

A controlled experiment methodology that compares two versions of a product, feature, or experience to determine which performs better against a defined metric.

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

The ability to understand, diagnose, and resolve data quality issues across the data stack by monitoring freshness, distribution, volume, schema, and lineage of data assets.

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

The practice of collaboration between data science and operations to automate and manage the machine learning lifecycle.

Online Analytical Processing — a category of software tools enabling analysis of data stored in databases for business intelligence.

The documentation of data's origins, movements, and transformations throughout its lifecycle.

Mining

The process of using computational power to validate transactions and add new blocks to a proof-of-work blockchain.