Navigating complex health data challenges

This course equips you with the essential skills to manage health data projects effectively within trusted research environments. You will start by exploring the key stages of using patient data responsibly, from project initiation to implementation. In Module 1, you will be learning about stages of a health data science project in trusted research environment and how to use FINER and PICO to develop your specific research question. You’ll also learn the essential elements for translation of research questions to database languages and required data linkage approaches. Module 2 will focus on project development aspect and open source development. You will learn about agile capabilities tools and best practices for healthcare data science projects. As part of this you will also look at codifying research questions and achieving research ready data assets. In Module 3, there is a major focus on generating reproducible healthcare data science project reports. You will go through a real-world example and mapping it to what it would mean in the context of your independent project. You will also learn about tools such as RMarkdown, high performance computing and their use cases for large scale data science projects. By the end of this course, you'll be equipped with the knowledge and skills required for effective development of health data projects within trusted research environments. 3b

• Module 1:● Describe stages of projects in TREs.● Outline the use of FINER and PICO criteria in evaluation and construction of research questions● Developing a SQL for basic data interrogation ● Identify and evaluate key factors involved in healthcare data linkageModule 2:● Identify core capabilities of Agile development● Identify considerations for optimised database design ● Use metadata and good coding practices to enhance flow of a healthcare data science project ● Assess the impact of employing specific version control tools on the efficiency and accuracy of data analysis.Module 3:● Outline key components and objectives in a hands-on health data science project ● Explain the concept of premature optimisation and its impact on project development efficiency.● Apply Markdown syntax to create and format an RMD document for data analysis reporting.● Conduct exploratory data analysis and visualize findings to identify trends and outliers.Gain a principal understanding of high-performance computing (HPC) and its use in research.

• Prior coding experiences as well as knowledge of R and SQL would be desirable but the codes provided is simplified and you should be able to follow along.