Clinical Data Science is an evolution of Clinical Data Management. Clinical Data Science encompasses processes, domain expertise, technologies, data analytics, and Good Clinical Data Management Practices essential to prompt decision-making throughout the life cycle of Clinical Research. Clinical Data Science can be defined as the strategic discipline enabling the execution of complex protocol designs in a patient-centric, data-driven and risk-based approach ensuring subject protection as well as the reliability and credibility of trial results.
Data Engineering
This unit introduces you to data wrangling and provides you with an understanding of structured and unstructured data formats, how data is modelled in various commonly used database systems, as well as an awareness of data/cyber security.
Maths, Stats / Machine Learning/ Artificial Intelligence
You will cover data analysis methods, including statistical learning (statistics and machine learning methods) supported by knowledge and understanding of the mathematical principles underpinning these methods.
Data Visualisation and Communication
This unit focuses on the theories of visualisation and how to explore and communicate data through visualisations that can be tailored for different audiences without unintentionally misleading or confusing the intended recipient.
Human Factors and Digital Transformation
You will be provided with an overview of the process of capturing and presenting user requirements and implementing and evaluating systems in the clinical, health and social care environment.
- Analytical Methods Applied to Data
- Decentralized Clinical Trials
- Artificial Intelligence
- Advanced Drug Development Concepts
- Process Automation