Data analysis is the process of inspecting, cleansing, transforming and modelling data with the goal of discovering actionable insights, informing conclusions and supporting evidence-based decision-making. Analysis can be descriptive (what happened in the past), diagnostic (understanding why something in the past happened), predictive (what will happen in the future) and/or prescriptive (assessing various options to recommend a future course of action) in nature, depending on analytical requirements.
We take a user-led approach to data analysis. We do this by placing the end user at the centre of the end-to-end lifecycle of analytical projects, from gathering requirements and data collection to statistical modelling and data visualisation. Our approach ensures that analytical projects deliver relevant, meaningful and actionable insights, whilst enhancing the end user's self-service ability to access, visualise, search, explore and interpret analytical data.
Statistical learning is a framework for machine learning and artificial intelligence. The goal of statistical learning is to learn from data, using mathematical techniques, in order to provide predictions of future events, behaviours, patterns and trends. Statistical learning transcends nearly all major industries and is employed across a rich and varied range of use-cases such as predicting disease risk factors, fraud and spam detection, image and speech recognition, predicting stock prices, and customer product recommendation engines.
We take a multidisciplinary, human-centered approach to designing statistical learning models, meaning that we involve not just data scientists but also designers, researchers, engineers, testers and SMEs as equal partners across the end-to-end lifecycle of intelligent systems development. Our approach ensures that machine learning and artificial intelligence systems, powered by statistical learning models, create genuine and lasting value to both humans and the environment whilst minimising bias and delivering diverse innovation.
Artificial intelligence (AI) concerns the design and development of machines capable of performing tasks that typically require human intelligence. Machine learning (ML) is a branch of artificial intelligence that uses statistical learning models to learn from data (typically vast volumes of structured and unstructured data and in real-time) in order to provide predictions of future events. Deep learning (DL) is a subset of machine learning that uses artificial neural networks, inspired by the architecture of the human brain, to learn from data.
Our approach to the design, development and evaluation of AI systems is underpinned and informed by principles for the creation of responsible AI. These principles include safety (sufficient safeguards to ensure the safety of all stakeholders), equality (treating all individuals equally), privacy (maintaining data privacy & security), transparency (making system & algorithmic design and AI decisions available for external scrutiny), accountability (conducting risk and impact assessments and for stakeholders to take responsibility for the actions of AI systems) and positive values (promoting positive human values).