Learner Profiles
Who Should Take This Workshop?
This workshop is designed for researchers learning Python programming for scientific analysis. Below are three representative learner profiles. Do you see yourself in one of these?
Profile 1: Aisha - Neuroscience Student with Excel Experience
Background
Aisha is a junior neuroscience major at Pomona working in the cognitive neuroscience lab. For the past year, she’s analyzed behavioral data using Excel—formulas, pivot tables, graphing. Now she’s working on her senior thesis involving brain imaging analysis with 500+ data files. Excel is too slow, and her lab mentor says she needs to learn Python to process the images and extract meaningful statistics.
What She Knows
- Excellent understanding of data, statistics, and analysis
- Proficient with Excel (formulas, conditional logic)
- Familiar with research design and experimental design
- Never written a computer program
- Understands the problem deeply; just needs the tool
Why She Needs This
- Excel can’t handle 500 neuroimaging files
- Lab has standard Python pipeline for image processing
- Manual analysis will take months; Python could do it in hours
- Needs to automate statistical calculations on large datasets
- Lab culture is moving toward computational methods
Her Goals
- Automate repetitive data processing
- Learn to write clean, understandable code
- Understand what her lab’s Python pipeline does
- Analyze her thesis data efficiently
- Prepare for graduate school computational work
Challenges She’ll Face
- Thinking in terms of algorithms instead of spreadsheets
- Understanding that code logic works differently than Excel
- Patience with debugging (Excel errors are usually obvious)
- Typing and syntax details matter (different from Excel)
- Frustrated if initial code doesn’t “just work”
Success Indicator
After this workshop, Aisha can: - Write simple Python scripts to process data files - Use variables, loops, and conditional logic - Call functions from libraries (like NumPy) - Read data from files and write results to new files - Debug basic Python errors - Feel confident enough to learn more advanced Python
Profile 2: Ryan - Economics Major Who Learned R
Background
Ryan is a senior economics major at Pomona doing statistical research on labor market data. In his econometrics courses, he learned R for statistical analysis. He’s comfortable programming in R, understands data frames and plotting, and knows good coding practices. His professor wants the lab to move to Python for a new research project, and Ryan wants to make the transition.
What He Knows
- Proficient programmer in R
- Understands data analysis workflow and statistics
- Familiar with libraries/packages and how to use them
- Knows coding best practices (comments, variable names, etc.)
- Has experience with loops, functions, and basic debugging
Why He Needs This
- Lab is shifting to Python for a collaborative project
- Python skills are more marketable in finance/tech careers
- Wants to rewrite his analysis pipeline in Python
- R and Python have different strengths; wants both skills
- Graduate school likely expects Python proficiency
Her Goals
- Transition from R to Python smoothly
- Understand Python equivalents of R functions
- Write clean, Pythonic code
- Contribute to lab’s Python projects
- Prepare for careers in data science/economics
Profile 3: Elena - Environmental Science Researcher, No Prior Programming
Background
Elena is a graduate student in environmental science studying soil chemistry and microbial communities. She’s designed a sophisticated field sampling methodology and collected 400+ soil samples with multiple chemical measurements. Now she has a large, complex dataset that needs quality control, statistical analysis, and visualization. She’s never programmed but knows she needs to learn Python to handle the volume of data and the complexity of analysis.
What She Knows
- Deep domain expertise in soil science and microbiology
- Understands research methodology and experimental design
- Comfortable with statistics conceptually
- Never written code or used programming language
- High motivation: this is essential for her research
Why She Needs This
- Dataset is too large and complex for spreadsheets
- Soil chemistry analysis requires numerical methods
- Wants to share reproducible analysis with collaborators
- Lab uses Python; colleagues can help if she learns it
- Needs this for thesis completion
Her Goals
- Analyze and visualize her soil chemistry data
- Automate quality control checks
- Run statistical tests on multiple samples
- Create publication-quality figures
- Understand Python well enough to ask for help
Challenges She’ll Face
- Impostor syndrome: “I’m not a programmer”
- Thinking algorithmically (very different from lab thinking)
- Frustration with syntax and error messages
- Fear that one mistake could corrupt data
- Difficulty separating coding concepts from domain knowledge
Success Indicator
After this workshop, Elena can: - Write Python scripts to load and clean data - Use loops and conditionals for quality control - Call scientific libraries (NumPy, SciPy) - Create simple visualizations of results - Troubleshoot basic Python errors - Ask informed questions of more experienced programmers
Using These Profiles
During teaching: - Reference Aisha when explaining loops: “Like analyzing each of your 500 brain scans…” - Use Ryan for comparative examples: “In R you’d use a data.frame; in Python…” - Draw on Elena’s domain expertise for examples: “If we’re checking soil sample quality…”
When explaining concepts: - Use domain-specific examples (imaging, economics, environmental science) - Connect programming concepts to research workflows - Show how Python code parallels the analysis steps they already think about
For pacing: - Aisha needs concrete connections to her analysis - Ryan might want advanced topics but shouldn’t skip foundations - Elena needs extra encouragement and reassurance
Common Threads
Despite different backgrounds, all three learners share:
- Real data to analyze: Not toy datasets
- Motivation from research: Each has a concrete need
- Some domain expertise: They understand their field
- Data challenge: Their current tool is insufficient
- Hope that Python will help: But not sure exactly how
Teaching to Diverse Learner Types
What Works Well
- Authentic datasets: Use real Pomona research data when possible
- Domain-specific examples: Environmental science code looks different from economics code
- Show the before/after: “Before Python: spreadsheet chaos; After Python: automated pipeline”
- Celebrate small wins: “You just processed 100 files with 5 lines of code!”
- Normalize struggle: “Everyone feels confused by error messages at first”
- Pair programming: Have learners work together; reduces anxiety
Pacing Tips
- For confident learners like Ryan: Provide comparison exercises, advanced libraries
- For practical learners like Aisha: Show immediate applications to her thesis
- For anxious learners like Elena: More practice time, explicit confidence-building
- For all: Celebrate understanding (“You understand list comprehensions!”)
After the Workshop
These learners won’t be Python experts, but they will:
- Write simple Python scripts for data analysis
- Use scientific libraries (NumPy, Pandas, Matplotlib)
- Read data, process it, and save results
- Understand basic programming concepts
- Know where to find help and resources
Many will return for: - Intermediate Python (debugging, testing, better practices) - Pandas for Data Analysis (more sophisticated data work) - Scientific Python (SciPy, machine learning) - Workshop 6: Automation with Make (pipeline automation)
Your role is to get them comfortable enough to write their own analysis scripts and confident enough to learn more.
Inspiration and Connection
Emphasize that:
- Elena’s 400 soil samples can be analyzed in minutes, not weeks
- Aisha’s 500 brain images can be processed overnight while she sleeps
- Ryan’s economics analysis becomes reproducible and shareable
- All their research becomes more efficient and more reliable