Bayesian Statistics

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.

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

Artificially generated data that mimics the statistical properties of real-world data for training and testing.

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

Artificially generated datasets that preserve the statistical properties of real data while protecting privacy, used for testing, development, and sharing across organisational boundaries.

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

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

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

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