Data Science

What is Statistical Learning?

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.

Our Methodology

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.

Benefits - Better Decisions

By taking a multidisciplinary, human-centered approach to designing statistical learning models, organisations will be able to:

• More accurately predict future events whilst minimising bias.
• Make better and more effective evidence-based decisions.
• Get hands-on with the toolkits that deliver artificial intelligence.
• Utilise a diverse range of skills to deliver innovation.

What is Artificial Intelligence?

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 Methodology

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).

Benefits - Responsible Insights

By designing artificial intelligence systems informed by principles for the creation of responsible AI, organisations will be able to:

• Discover and generate new actionable insights in real-time.
• Make fair & legal decisions supported by responsible frameworks.
• Intelligently automate business processes to reduce costs.
• Augment and optimise workflows and reduce human error.