Time Series 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.

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 study of graphs representing relationships between discrete objects to understand network structure and dynamics.

The process of examining, analysing, and creating summaries of data to assess quality and structure.

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

The process of labelling data with informative tags to make it usable for training supervised machine learning models.

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

Statistical and machine learning methods for predicting future values based on historical sequential data, applied to demand planning, financial forecasting, and resource allocation.

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

The analysis of geographic and spatial data to discover patterns, relationships, and trends tied to location.