Learner Profiles
The training is designed to cater to a wide range of learners, ensuring that everyone can build a strong foundation in Python programming for data analysis. Below are key learner profiles who will benefit from the course in different ways:
Profile 1: Wet Lab Scientists Transitioning to Data Analysis
- Background:
- Learners with no previous experience in Python, transitioning from experimental/laboratory-based research toward computational data analysis.
- Motivation:
- To enhance research through skills in data wrangling, statistical analysis, and visualisation using Python.
- Challenges:
- Unfamiliarity with basic programming logic and syntax.
- Difficulty navigating Python development environments (e.g. Jupyter, VS Code).
- Understanding core data types, control flow, and library usage.
- Unfamiliarity with basic programming logic and syntax.
- Learning Objectives:
- Understand fundamental Python programming concepts.
- Load, clean, and manipulate data using
pandas.
- Build confidence in using Jupyter notebooks for transparent, reproducible workflows.
- Understand fundamental Python programming concepts.
Profile 2: Programmers New to Python for Data Science
- Background:
- Learners with programming experience (e.g., R, MATLAB, Java) but limited exposure to Python for data science.
- Motivation:
- To translate general coding knowledge into data-focused Python workflows using
numpy,pandas,matplotlib, and related tools.
- To translate general coding knowledge into data-focused Python workflows using
- Challenges:
- Adjusting to Python’s syntax, ecosystem, and object-oriented design.
- Understanding idiomatic Python for data operations.
- Navigating the Python packaging and environment system (e.g.
venv,pip,conda).
- Adjusting to Python’s syntax, ecosystem, and object-oriented design.
- Learning Objectives:
- Learn the Pythonic way of writing clean, maintainable code.
- Gain fluency in common libraries for data analysis.
- Integrate Python into multi-language analysis pipelines.
- Learn the Pythonic way of writing clean, maintainable code.
Profile 3: Data Scientists Expanding into Research Applications
- Background:
- Practitioners with experience in Python and data science, looking to apply these skills in a research context (e.g. life sciences, social sciences).
- Motivation:
- To apply Python tools to real-world, domain-specific research datasets.
- To adopt best practices in reproducibility, documentation, and pipeline development.
- To apply Python tools to real-world, domain-specific research datasets.
- Challenges:
- Adapting general-purpose data workflows to discipline-specific challenges.
- Understanding nuances of scientific data formats and metadata.
- Producing research outputs that meet academic and publishing standards.
- Adapting general-purpose data workflows to discipline-specific challenges.
- Learning Objectives:
- Work confidently with structured and semi-structured research data.
- Use Python to generate reproducible, shareable analysis outputs.
- Apply good research software practices (e.g. testing, version control, documentation).
- Work confidently with structured and semi-structured research data.