Privacy-Preserving 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.

An abstraction layer that provides business-friendly definitions and consistent metrics on top of raw data, enabling self-service analytics with standardised terminology.

The process of analysing customer attrition to understand why customers stop using a product or service.

An automated workflow that extracts data from sources, transforms it according to business rules, and loads it into a target system.

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

An automated set of processes that moves and transforms data from source systems to target destinations.

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

Tracking and analysing the sequential steps users take toward a desired action to identify drop-off points.

Federated Learning

A machine learning approach where models are trained across decentralised devices without sharing raw data, preserving privacy.