Course Description
This course begins with a Python and Pandas refresher, covering data types, control flow, file handling and clinical trial dataset structures such as CDISC SDTM. It then moves into advanced Python concepts including object-oriented programming, reusable modules, error handling and logging for building automation tools. Subsequent modules cover advanced Pandas operations, data validation against CRF specifications, discrepancy reporting and Excel styling, followed by automating multi-file processing, scheduling tasks, generating reports with charts, and building Streamlit dashboards. The course concludes with a hands-on workshop to design and build a clinical data automation tool, including API integration, PHI anonymisation and documentation practices.
Audience
- Clinical Data Analysts
- Clinical Programmers
- Data Validation Specialists
- Clinical Trial Reporting Staff
- Automation Developers
Objectives
- Apply Python data types, control flow and functionsto clinical data tasks
- Use Pandas to load, filter, sort and summarisetrial datasets
- Implement object-oriented programming for buildingautomation tools
- Apply error handling and logging for audit trails
- Validate datasets against CRF specifications anddetect discrepancies
- Build discrepancy and summary reports usingopenpyxl and xlsxwriter
- Automate multi-file processing and scheduleddata tasks
- Develop Streamlit dashboards for visualisingclinical metrics
Prerequisites
- Foundational Certification: Completion of PCEP (Certified Entry-Level Python Programmer) or PCAP (Certified Associate in Python Programming) by the Python Institute is strongly recommended as a benchmark.
- Technical Proficiency: Ability to write scripts using list comprehensions, lambda functions, and custom modules to manipulate complex data structures.
- Environment Requirements: Proficiency in setting up and managing a local development environment using Anaconda/Conda, with experience in Jupyter Notebooks and Python 3.9+.
- Data Handling: Familiarity with core Python libraries, specifically Pandas and NumPy, for the processing and cleaning of clinical datasets.
- Statistical Literacy: Understanding of fundamental descriptive statistics concepts required for accurate clinical data analysis.
- Domain Knowledge: A general understanding of medical data concepts, including Electronic Health Records (EHR) and regulatory standards, to ensure compliance and security in the Python Programming for Clinical Medical Domain course by the Python Institute.
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