Learner Profiles

Typical Participants

Profile 1: Biology Graduate Student Analyzing Comparative Data

Name: Sarah Kim Background: 2nd year PhD student in comparative biology, working in a Pomona College biology lab and storing project files on Sagehen at /bigdata/lab/biolab/

Motivation: - Analyzing species biodiversity data across continents (and a side project on the Bernard Field Station Cecil Sagehen population) - Has 50+ CSV files with country-level GDP and health data - Needs to combine, visualize, and analyze relationships - Advisor expects publication-ready plots and is requesting that Sarah’s notebooks run unchanged on Sagehen via OnDemand

Experience: - Comfortable with basic statistics and scientific thinking - Recently completed intro programming course (used Python basics) - Can write simple for loops and if statements - No experience with data visualization - Never used pandas or matplotlib

Pain Point: “I have all this data in spreadsheets, but I don’t know how to load it into Python or make plots that look professional. My advisor showed me matplotlib plots online, and they looked great, but I have no idea where to start.”

Expected Outcome After Workshop: - Load CSV files with pandas - Create publication-quality plots with matplotlib - Manipulate and filter dataframes - Combine multiple datasets - Save plots in publication-ready formats

Preference: Will use Jupyter Notebook on the Pomona OnDemand portal at https://ondemand.hpc.pomona.edu, signed in with her Pomona DUO MFA at https://duo.pomona.edu

Profile 2: Psychology Researcher Building Data Processing Scripts

Name: James Okonkwo Background: Postdoc in the Pomona College psychology department

Motivation: - Processing behavioral experiment data (reaction times, accuracy) from Pomona undergraduate participants - Runs 20+ experiments per year, scheduled across the academic calendar - Wants to automate plotting and statistical summaries so weekly figures are produced as a SLURM batch job on Sagehen HPC - Currently uses Excel, wants to move to Python

Experience: - Familiar with R and basic statistics - Some Python programming experience - Can write functions and basic scripts - Never used pandas or matplotlib - Has moderate data handling needs

Pain Point: “I can write Python code, but I don’t know how to work with data tables in Python. I also don’t know how to make the plots my papers require without hours of manual work in Illustrator.”

Expected Outcome After Workshop: - Use pandas for data manipulation and filtering - Create multiple plot types (scatter, bar, line) - Automate plot creation for batch processing - Understand when to use different plot types - Customize plots for publication

Preference: Will use Jupyter Notebook, may write standalone Python scripts later

Profile 3: Environmental Science Undergraduate Learning Research Skills

Name: Alex Patel Background: Junior undergrad in environmental analysis at Pomona College, doing fieldwork at the Robert J. Bernard Biological Field Station

Motivation: - Starting undergraduate research project on climate and agriculture - Needs to learn data visualization for research reports for an environmental analysis senior thesis - Planning to pursue graduate study in environmental science - First real programming experience for science; will use the Sagehen HPC cluster for any analyses too large for a laptop

Experience: - Beginner programmer (some high school CS, intro college course) - Took basic statistics class - Comfortable with spreadsheets - No prior experience with Python scientific libraries - Enthusiastic but anxious about coding

Pain Point: “I know what data I want to analyze, but I don’t know how to turn a CSV file into a plot. I’m worried the code will be too complicated for me.”

Expected Outcome After Workshop: - Load and explore CSV data - Create basic plots (scatter, line, histogram) - Understand when to use matplotlib vs. pandas plotting - Gain confidence in Python for research - Know where to find help and examples online

Preference: Will use Jupyter Notebook with lots of examples and step-by-step guidance, launched through the Pomona OnDemand portal so Alex never has to install Python locally

Common Learner Characteristics

What They Know

  • Basic Python syntax (variables, loops, conditionals)
  • How to open files in a code editor
  • Basic statistics (mean, standard deviation)
  • What data visualization is and why it matters

What They Don’t Know

  • How to load CSV files into Python
  • How pandas DataFrames work or when to use them
  • How to create plots beyond simple matplotlib calls
  • How to customize plots for publication
  • How to work with multiple datasets together
  • Best practices for data visualization

What They Value

  • Clear examples: Want to see real, working code they can modify
  • Practical application: Need solutions to their actual research problems
  • Quick results: Want to see plots early in the workshop
  • Publication quality: Need plots suitable for papers and presentations
  • Flexibility: Prefer tools that work across different types of data

What They Worry About

  • “Will this be too technical?” “Can I actually do this?”
  • “How do I debug code?” “What if something breaks?”
  • “Will I understand data frames?” “They sound complicated”
  • “Are there better tools?” “Should I use R instead?”
  • “How do I make professional-looking plots?” “My plots look ugly”

Design Philosophy

This workshop is designed for researchers who have data and want to visualize and analyze it in Python, not for programming language specialists.

Key assumptions: - Participants can read and write basic Python code - Participants have real data they want to work with - Participants value practical skills over theory - Participants may not have computer science background

The workshop emphasizes: - “How do I load MY data?” not “how do CSV files work?” - “How do I make MY plot?” not “how does matplotlib rendering work?” - “What plot type should I use?” by showing examples - “Can I make it publication-quality?” through customization - “What’s the minimum code I need?” to avoid overwhelming examples

By day’s end, participants should have either: 1. Created publication-quality plots from their own or provided data, OR 2. Clear understanding of the pandas/matplotlib workflow for their data