Time series decomposition is a method used to break down a sequence of data points measured over time into several distinct components. These components typically include the trend, which shows the long-term direction, the seasonality, which reflects repeating patterns, and the residual or noise, which captures random variation. By separating a time series into these…
Category: Data Science
Statistical Model Validation
Statistical model validation is the process of checking whether a statistical model accurately represents the data it is intended to explain or predict. It involves assessing how well the model performs on new, unseen data, not just the data used to build it. Validation helps ensure that the model’s results are trustworthy and not just…
Data Preprocessing Pipelines
Data preprocessing pipelines are step-by-step procedures used to clean and prepare raw data before it is analysed or used by machine learning models. These pipelines automate tasks such as removing errors, filling in missing values, transforming formats, and scaling data. By organising these steps into a pipeline, data scientists ensure consistency and efficiency, making it…
Feature Importance Analysis
Feature importance analysis is a technique used in data science and machine learning to determine which input variables, or features, have the most influence on the predictions of a model. By identifying the most significant features, analysts can better understand how a model makes decisions and potentially improve its performance. This process also helps to…
Data Sampling Strategies
Data sampling strategies are methods used to select a smaller group of data from a larger dataset. This smaller group, or sample, is chosen so that it represents the characteristics of the whole dataset as closely as possible. Proper sampling helps reduce the amount of data to process while still allowing accurate analysis and conclusions.
Process Automation Analytics
Process automation analytics refers to the use of data analysis tools and techniques to monitor, measure, and improve automated business processes. It helps organisations understand how well their automated workflows are performing by collecting and analysing data on efficiency, errors, and bottlenecks. This insight allows businesses to make informed decisions, optimise processes, and achieve better…
Knowledge Graph Completion
Knowledge graph completion is the process of filling in missing information or relationships within a knowledge graph. A knowledge graph is a structured network of facts, where entities like people, places, or things are connected by relationships. Because real-world data is often incomplete, algorithms are used to predict and add missing links or facts, making…
Graph Signal Processing
Graph Signal Processing (GSP) is a field that studies how to analyse and process data that lives on graphs, such as social networks or transportation systems. It extends traditional signal processing, which deals with time or space signals, to more complex structures where data points are connected in irregular ways. GSP helps to uncover patterns,…
Model Performance Tracking
Model performance tracking is the process of monitoring how well a machine learning model is working over time. It involves collecting and analysing data on the model’s predictions to see if it is still accurate and reliable. This helps teams spot problems early and make improvements when needed.
Data Quality Monitoring
Data quality monitoring is the process of regularly checking and assessing data to ensure it is accurate, complete, consistent, and reliable. This involves setting up rules or standards that data should meet and using tools to automatically detect issues or errors. By monitoring data quality, organisations can fix problems early and maintain trust in their…