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This module covers essential elements needed for health data scientists to use python for analysis and scientific presentation of complex health data. In this module you will learn how to construct reliable, readable, efficient analytical procedures for data-intensive research in a collaborative environment. You will learn to use libraries, coding conventions, functional and object-oriented programming, visualisation. You will learn techniques essential for reproducible research including version control. The module will emphasise practical techniques. It is a practical course, which will train you in techniques that are essential for health data scientists.

At the end of the module, you should be able to:

1.ÌýÌýÌý Understand the fundamental principles of python programming, algorithms, including knowledge of the main data types and structures.

2.ÌýÌýÌý Apply conditionals and loops as well as functions for code reproducibility and use key modules essential for health data science including; numpy, pandas, re, and matplotlib.

3.ÌýÌýÌý Perform basic statistics and data visualisation of complex health-related datasets.

4.ÌýÌýÌý Understand object-oriented programming, version controlling and high-performance computing.

5.ÌýÌýÌý Critically appraise solutions, be able to argue for and against possible solutions and describe and discuss the results of your analyses.

Delivery Mode should be:

You will learn by a combination of lectures, practical problem classes and structured independent learning. You will therefore require a laptop that you can use to write python code, execute scripts, and connect to databases; tools we will use include Python 3. Anaconda, and Jupyter notebooks. Tablets are not suitable as they do not offer the necessary underlying infrastructure to install and run the needed software.

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