Regression 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.

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.

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

The analysis of historical data to understand what has happened in the past and identify patterns.

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

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

Statistical analysis measuring the strength and direction of the relationship between two or more variables.

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

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

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