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Python is increasingly being used for data science in biomedicine. This introductory and interactive course is run over one day and will cover the basic aspects of programming in Python.
The interactive lecture sessions serve as a opportunity to explore the concepts in greater depth, raise questions, and enable participants to acquire greater understanding of the Python programming language. Throughout this course we will use examples from biomedicine.
Learning Objectives
By the end of this course, participants will be able to:
- Explain the principles of Python as a programming language
- Outline and apply the interaction with Python
- Define and use basic elements of Python such as variables and expressions/operators
- Explain and apply data types such as numbers, strings, lists, sets, dictionaries, namespaces, and tuples
- Outline and use if-statements (flow controls)
- Present and practice loop-constructs in Python
- Describe the syntax of a function and demonstrate the use of functions
- Demonstrate error handling in Python
- Present the essentials of modules, libraries, file input and output, and best practices
- Apply Python in one data science problem of healthcare
Planned Timetable
| Time | Title of Session |
|---|---|
| 09:00-09:30 | Registration and coffee |
| 09:30-11:00 | 1) Introduction into Python 2) Interaction with Python 3) Expressions and operators 4) Data elements and data types |
| 11:00 -11:15 | Coffee |
| 11:15-12:45 | 5) Flow Controls 8) Error handling |
| 12:45 - 13:45 | Lunch |
| 13:45-15:15 | 9) Modules and libraries |
| 15:15 - 15:30 | Coffee |
| 15:30-17:00 | 10) Python for Data Science in Healthcare |
Course Team
- Dr Holger Kunz (Lead Tutor)
- Holger is a Teaching Fellow at the UCL Institute of Health Informatics. He studied informatics at the Institute of Mathematics and Informatics of the Freie University Berlin and received a doctoral degree in medical informatics from Charité University Medicine Berlin – Europe’s largest academic hospital.
He has conducted research in applied machine learning for medical imaging and the treatment of eye tumours. He has also conducted data science research for clinical indicator systems and quality management/dashboards in a hospital setting.
He has presented his research at international conferences in Vancouver, Sydney, Portland, Lyon and Glasgow.
Before joining UCL, he worked at Imperial College London in the School of Public Health in the field of eHealth for health and wellbeing and disease prevention. He is passionate about health informatics and about using methods of informatics/computer science to improve the health of people and populations.