ATOC 4815/5815

Scientific Programming, Data Analysis & Visualization

Will Chapman

CU Boulder ATOC

2026-01-01

Welcome!

Scientific Programming, Data Analysis & Visualization

Spring 2026

Who We Are

Your Instructor: Dr. Will Chapman

Education:

  • B.Sc. Environmental Engineering - UC San Diego
  • M.Sc. Civil & Environmental Engineering - Stanford University
  • Ph.D. Oceanography - Scripps Institution of Oceanography
  • Postdoc - NCAR Advanced Study Program

Research Expertise:

  • Earth System Predictability
  • Atmospheric Modeling & Coupled ESM
  • Statistics for Earth Systems
  • Machine Learning for Climate

Previous Lives:

Energy Consultant • NCAR Project Scientist • Photographer • Musician • Woodworker • Ping-Pong Table Maker • Pizza Maker • Baseball Field Tender

Call me: Will (or Dr. Chapman if you prefer)

Office Hours: Tu/Th 11:15-12:15p (Aerospace Café)

Important Announcement

  • This is the first time I’m teaching this class
  • That means:
    • We’re figuring this out together
    • I will make mistakes (and learn from them)
    • Your feedback is crucial
    • I’m excited to try new pedagogical approaches
  • What this means for you:
    • Be patient with me
    • Speak up if something isn’t working
    • Help shape this course for future students
    • We’re building something together

My promise to you: I will work hard to make this valuable for your career.

Your TA: Aiden Pape

Education:

  • B.A. Computer Science - Middlebury College
  • Ph.D. Machine Learning and Data Assimilation [in progress]

Interests:

  • Soccer, skiing, climbing
  • Live music
  • Pets

Call me: Aiden

Office Hours: M/W 4-5p (DUAN D391)

Support & Resources

Campus Resources

If you are struggling there are resources:

Basic Needs

Mental Health

Academic Support:

My door is always open (SEEC N258). If you’re struggling, please talk to me.

Academic Integrity

This class follows CU’s Honor Code

What collaboration looks like in this class:

  • ✅ Encouraged: Discussing concepts with classmates
  • ✅ Encouraged: Helping each other debug code
  • ✅ Encouraged: Sharing resources and learning strategies
  • ⚠️ Gray area: Using AI tools (we’ll discuss this next)
  • ❌ Not allowed: Copying homework solutions
  • ❌ Not allowed: Sharing your code for others to copy
  • ❌ Not allowed: Using someone else’s work without attribution

When in doubt, ask! I’d rather clarify than have you worried.

The Elephant in the Room

Let’s Talk About LLMs / AI

Large Language Models (ChatGPT, Claude, Copilot) are here to stay.

My philosophy:

  1. AI is a tool, not a crutch
    • Like a calculator: you still need to understand math
    • Like Stack Overflow: useful, but you need to understand what you’re using
  2. You MUST understand the code you submit
    • I will ask you to explain your solutions
    • Exams are in-person, no AI
  3. AI can help you learn FASTER—if used correctly
    • Explaining errors
    • Suggesting alternative approaches
    • Finding documentation
  4. AI can also prevent you from learning—if used incorrectly
    • Copy-pasting without understanding
    • Using it as a “do my homework” button

AI Use Policy for This Class

For the first 8 weeks AI is not permitted
After the first 8 weeks:

✅ Allowed:

  • Using AI to explain error messages
  • Asking AI to suggest debugging strategies
  • Using AI to find relevant documentation
  • Using Copilot for autocomplete suggestions
  • Discussing concepts with AI (like a study partner)

❌ Not Allowed:

  • Copy-pasting homework prompts into AI and submitting the output
  • Using AI to write entire functions without understanding them
  • Relying on AI during exams (they’re in-person anyway)

⚠️ Gray Area → Ask First:

  • Having AI generate starter code
  • Using AI to refactor your working code

Bottom line: If you can’t explain your code line-by-line, you don’t understand it well enough.

Course Overview

What You’ll Learn

This course will prepare you for software-intensive geoscience careers

Week-by-week progression:

Week Topic Key Skills
1 Python Fundamentals Variables, functions, control flow
2 NumPy & Plotting Arrays, vectorization, matplotlib
3 Pandas & Time Series DataFrames, resampling, rolling windows
4 xarray & Gridded Data NetCDF, multi-dimensional arrays, climatologies
5 Mapping & Cartopy Projections, overlays, publication maps
6 Version Control (git) Repositories, commits, collaboration
7 Data I/O & Formats CSV, NetCDF, HDF5, APIs
8 Midterm 1
9 Statistics & Uncertainty Distributions, confidence intervals, bootstrapping
10 Linear Regression Fitting, residuals, diagnostics
11 Spectral Analysis FFT, power spectra, filtering
12 Midterm 2
13-15 Final Projects Real research workflows

Course Goals

By the end of this semester, you will be able to:

  1. Write production-quality Python code
    • Functions, classes, error handling
    • Reproducible workflows
    • Version-controlled projects
  2. Work with real atmospheric/oceanic datasets
    • ERA5 reanalysis
    • CMIP6 climate models
    • Satellite observations
    • Station measurements
  3. Create publication-quality visualizations
    • Time series plots
    • Maps with projections
    • Multi-panel figures
    • Professional styling
  4. Apply statistical methods to geoscience problems
    • Hypothesis testing
    • Regression analysis
    • Spectral analysis
    • Uncertainty quantification

Course Goals (continued)

  1. Collaborate using modern tools
    • Git/GitHub workflows
    • Code review
    • Documentation
    • Reproducible environments
  2. Debug effectively
    • Read error messages
    • Use print/breakpoints
    • Search documentation
    • Ask good questions
  3. Think like a data scientist
    • Data quality checks
    • Exploratory analysis
    • Validation strategies
    • Communication of results

These skills apply far beyond this class:

Graduate research • Climate modeling • Data science • Environmental consulting • Software engineering

Course Materials

Online Textbook: Runestone Academy

  • I’ve already created accounts for you
  • Username: your_identikey_sp26_atoc4815 (e.g., abcd1234_sp26_atoc4815)
  • Password: hellox ← Please reset this immediately!
  • Reading assignments (with embedded exercises)
  • First reading due Thursday, Jan 15th

Look at Runestone dashboard

Additional Resources:

  • Lecture slides (posted after class)
  • Lab notebooks (Jupyter .ipynb files)
  • Python Society for Atmospheric Science docs
  • Official Python/NumPy/pandas documentation

Everything is free and online. No textbook to buy!

Course Structure

How This Class Works [first 5/6 weeks]

Each week follows this pattern:

Before Tuesday:

  • Read assigned Runestone chapters
  • Complete embedded exercises
  • Watch any posted videos

Tuesday Class:

  • 30 min: Lecture (new concepts)
  • 45 min: Hands-on lab
  • Start homework in class

Before Thursday:

  • Continue working on lab/homework
  • Review lecture slides
  • Post questions on Canvas

Thursday Class:

  • 30 min: Lecture (new concepts/ Q&A / Review)
  • 45 min: Hands-on lab
  • Start homework in class
  • Get help from Will/Aiden

By Sunday 11:59pm:

  • Submit lab notebook (.ipynb)
  • Submit homework script (.py)
  • Both due to Canvas

Throughout the week:

  • Office hours (use them!)
  • Canvas discussion board
  • Work with classmates

Assignments Breakdown

Labs (12.5%):

  • Jupyter notebooks with guided exercises
  • Practice applying concepts from lecture
  • Designed to be ~75% completable in class
  • Graded on effort + correctness

Homework (12.5%):

  • Python scripts (.py files)
  • More independent problem-solving
  • Builds on lab concepts
  • Graded on correctness + code quality

In Class Tests (10%):

  • In-class tests on reading / topics (short, 5-10 min)
  • First test: Tuesday, Jan 21st

Attendance/Participation (10%):

  • Missing > 4 lectures [unexcused] increments this 10% down by 3% each time
  • Active participation in discussions
  • Asking/answering questions

Exams & Project

Midterm 1 (15%): Monday, February 3rd

  • Weeks 1-4 material
  • In-person, no AI/internet
  • Focus on fundamentals (Python, NumPy, pandas)

Midterm 2 (15%): Wednesday, March 12th

  • Weeks 5-8 material
  • In-person, no AI/internet
  • Focus on xarray, mapping, git, I/O

Final Project (25%): Due Monday, April 27th, 11:59pm

  • ~Teams of 2 (or individual for grad students)
  • Present findings (last week of class)
  • 25% of your grade—this is your chance to shine!

Conflicts with these dates? Let me know ASAP.

Grading Summary

Component Weight
Homework + Labs 25%
Attendance & Participation 10%
In-class quizzes 10%
Midterm 1 15%
Midterm 2 15%
Final Project 25%
Total 100%

Wait!

I will build in a curve (likely ~15%) Yes! 15% buffer built in. You can miss some assignments and still get an A.

Translation:

  • Consistent effort = A [I expect everyone here]
  • Some struggles but you learn = B
  • Minimal effort = Not passing

Let’s Get to Know Each Other

Interactive Introduction Game

On the whiteboard, write:

  1. Your name
  2. Programming languages you’ve used (include “tools” like SQL, Bash, etc.)
  3. Your comfort level with those languages:
Level Meaning
0.2 Can follow examples, modify small things
0.4 Can write from scratch with docs open
0.6 Productive, can debug non-trivial issues
0.8 Could teach it / deep fluency
1.0 I basically wrote the language
  1. Mark ✓ if you’ve used a terminal/command line before

Example:

Alice | Python (0.6), MATLAB (0.4), SQL (0.2) | ✓

Grab a marker and add your info!

The Question Number System

I had a hard time getting questions answered last semester.

New system I’m trying (you’re my test subjects):

When I ask a question in class:

  1. I’ll ask the question
  2. You get 10-20 seconds to think (maybe jot notes)
  3. I’ll say “Ready?”
  4. You hold up fingers: 0-5
  5. I scan the room for distribution
  6. I call on someone to answer
  7. We hold up numbers again (did it help?)

The scale:

# Meaning
0 I have no idea
1 I’ve heard the term but can’t explain it
2 I have a rough idea but not confident
3 I could kind of explain it
4 I can explain it clearly
5 I could explain it AND give an example or connect it to other things

Why This System?

Traditional classroom problem:

  • Someone asks a question
  • Awkward silence
  • Same 2-3 students always answer
  • Most students don’t engage

This system:

  • ✅ Forces everyone to self-assess
  • ✅ Gives me instant feedback on class understanding
  • ✅ Reduces pressure (just hold up a number!)
  • ✅ Identifies who can help explain to peers
  • ✅ Shows when I need to re-explain something

If you hate this, tell me! We can abandon it if it’s not working.

If you love this, also tell me! I’d like to know it’s helpful.

Let’s Try It: Practice Round

Question: What is a for loop?

Take 15 seconds to think…

Ready? Show me your numbers: 0-5

[I scan the room for distribution]

[I call on someone with a 3-4]

Answer: A for loop is a programming construct that repeats a block of code for each item in a sequence (like a list or range). For example, for i in range(5): would repeat the indented code 5 times, with i taking values 0, 1, 2, 3, 4. It’s how we process multiple items without writing the same code over and over.

Show numbers again—did that help?

Getting Started

Today’s Setup Tasks

We need to get your computer ready for the semester:

  1. Install VS Code (our code editor)

  2. Install Git (version control)

    • Windows users: Install Git Bash
    • Mac users: Git likely already installed

  3. Set up terminal in VS Code

    • Windows: Use Git Bash
    • Mac: Use default terminal (zsh or bash)

  4. Install conda environment

    mamba env create -f atoc-2025-lite.yml

    (This takes ~5 minutes)

VS Code Extensions (Required)

Install these extensions today:

PLACEHOLDER: Screenshot of VS Code extensions panel

Extension ID Purpose
Python ms-python.python Python language support
Pylance ms-python.vscode-pylance Fast Python IntelliSense
Jupyter ms-toolsai.jupyter Notebook support
Ruff charliermarsh.ruff Fast Python linter
Markdown All in One yzhang.markdown-all-in-one Markdown editing

To install:

  1. Open VS Code
  2. Click Extensions icon (left sidebar)
  3. Search for extension ID
  4. Click “Install”

Need help? Raise your hand! Will and Aiden will circulate.

Conda Environment Setup

What is conda?

  • Package manager for Python
  • Creates isolated environments (prevents conflicts)
  • Ensures everyone has same versions

Download the environment file:

Canvas → Files → atoc-2025-lite.yml

Then run:

# Navigate to download folder
cd ~/Downloads

# Create environment (takes ~5 min)
mamba env create -f atoc-2025-lite.yml

# Activate environment
conda activate atoc2025

# Verify installation
python --version  # Should see Python 3.11.x

Stuck? Don’t leave today without getting this working. Get help now!

Next Steps

What To Do This Week

By Tuesday, Jan 13th:

By Thursday, Jan 15th:

By Sunday, Jan 18th, 11:59pm:

Pro tip: Start homework in Thursday’s class—we’ll be there to help!

Where to Get Help

Stuck on something? Here’s the hierarchy:

  1. Try for 15 minutes on your own
    • Read the error message
    • Check lecture slides
    • Search documentation
  2. Ask a classmate
    • Slack/Discord/GroupMe
    • Study groups
    • Explain your issue (teaching helps you learn!)
  3. Post on Canvas Discussion Board
    • Others likely have the same question
    • Aiden and I monitor daily
    • Great for asynchronous help
  4. Come to Office Hours
    • Will: Tu/Th 11:15-12:15p (Aerospace Café)
    • Aiden: M/W 3:30-4:30p (DUAN D319)
  5. Then Email
    • wchapman@colorado.edu
    • Please include “[ATOC4815]” in subject
    • Allow 24 hours for response

Course Resources - All in One Place

Canvas: canvas.colorado.edu

  • Syllabus
  • Weekly modules
  • Assignment submissions
  • Grades
  • Announcements

Runestone: runestone.academy

  • Interactive textbook
  • Reading assignments
  • Embedded exercises

Course Website: willychap.github.io/slides

  • Lecture slides (HTML)
  • Code examples
  • Resources

GitHub: (We’ll set this up in Week 6)

  • Version control
  • Project collaboration

A Few Ground Rules

To make this class work for everyone:

  1. Laptops open during coding time, closed during lecture
    • Exception: taking notes
  2. Ask questions!
    • There are no stupid questions
    • If you’re confused, others are too
  3. Help each other
    • Collaboration is encouraged
    • Teaching others helps you learn
  4. Start assignments early
    • Don’t wait until Sunday night
    • Use office hours!
  5. Communicate with me
    • Struggling? Tell me.
    • Too easy? Tell me.
    • Something not working? Tell me.

One More Thing

Why Programming Matters for Your Career

This class is hard work.

Every employer I talk to says:

“We need people who can code and understand atmospheric science.”

That’s you, by the end of this semester.

My Promise to You

I promise:

  • I will make this class worth your time
  • I will be available and responsive
  • I will challenge you without overwhelming you
  • I will help you debug your code (even at 11pm on Sunday)
  • I will create a welcoming environment for all skill levels
  • I will listen to your feedback and adapt
  • I will prepare you for real research

What I ask from you:

  • Show up
  • Try hard
  • Ask questions
  • Help your classmates
  • Give me feedback
  • Be patient with me (first time teaching this!)

Questions?

Let’s Get Started!

Today (remaining time):

  1. Get VS Code installed
  2. Install Git/Git Bash
  3. Set up conda environment
  4. Test that everything works

Raise your hand if you need help at any point!

Aiden and I will circulate to help troubleshoot.

Welcome to ATOC 4815/5815!

Let’s make this an amazing semester.

Backup Slides

Detailed Schedule (Week-by-Week)

Week Dates Topic Lab/HW Reading
1 Jan 14-16 Python Fundamentals Variables, functions, loops Runestone Ch 1-3
2 Jan 21-23 NumPy & Visualization Array operations, matplotlib Runestone Ch 4-5
3 Jan 28-30 Pandas & Time Series DataFrames, resampling Runestone Ch 6
4 Feb 4-6 xarray & Gridded Data NetCDF, climatologies Canvas reading
5 Feb 11-13 Mapping & Cartopy Projections, overlays Canvas reading
6 Feb 18-20 Version Control (Git) Repos, commits, branches Canvas reading
7 Feb 25-27 Data I/O CSV, NetCDF, APIs Canvas reading
8 Mar 4-6 Midterm 1 Review Practice problems Review all
9 Mar 11-13 Statistics Distributions, uncertainty Canvas reading
10 Mar 18-20 Linear Regression Fitting, diagnostics Canvas reading
11 Mar 25-27 Spring Break No class Rest!
12 Apr 1-3 Spectral Analysis FFT, filtering Canvas reading
13 Apr 8-10 Midterm 2 Review Practice problems Review all
14 Apr 15-17 Project Work Time In-class work N/A
15 Apr 22-24 Project Presentations Final project due N/A

Software Installation Troubleshooting

Common Issues:

Issue: “conda: command not found”

  • Solution: Restart terminal after installing
  • Or: Add conda to PATH (instructions vary by OS)

Issue: “Permission denied” on Mac

  • Solution: Use sudo before command (requires admin password)

Issue: Git Bash not showing in VS Code on Windows

  • Solution: Open Settings → Terminal: Integrated → Default Profile → Select “Git Bash”

Issue: Environment creation fails

  • Solution: Try conda instead of mamba
  • Or: Update conda first: conda update conda

Issue: Extensions won’t install

  • Solution: Restart VS Code
  • Or: Check internet connection
  • Or: Install manually from .vsix file

Still stuck? Slack me a screenshot of the error!