Data Engineering

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.

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.

Market Basket Analysis

A data mining technique discovering associations between items frequently purchased together.

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

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

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

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 collecting and analysing customer data to understand behaviour, preferences, and lifetime value.

A reusable, well-documented, and managed dataset or analytical asset created to serve specific business needs, treated with the same rigour as software products.

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

Using NLP techniques to extract insights and sentiment from unstructured text data at scale.