Home - AI-Augmented Data Science for Climate Leadership

CSOL-208: Data Science for Climate Solutions

Spring 2026 • 2 Units

schedule Monday & Wednesday, 12:10pm - 1:50pm

location_on 311 Wellman Hall

This course integrates data science fundamentals into the climate solutions curriculum, reframing them for an AI-driven era. Students practice data organization, visualization, and quantitative analysis by communicating in natural language with AI systems. We explore modern code-developemnt clients, MCP tooling, LLM API use, local models, and development of AI agents. Alongside hands-on, team based projects, we also critically examine AI's reliability, ethics, and environmental impacts.

This course is part of the Master of Climate Solutions program at UC Berkeley, a professional degree designed to train the next generation of climate leaders. The MCS program combines rigorous technical training with practical skills in policy, finance, and leadership to address the climate crisis. Students interested in the program can learn more and apply at climatesolutions.berkeley.edu.

Instructors

Carl Boettiger

Associate Professor, ESPM

Carl works on ecological forecasting and decision making under uncertainty, focusing on regime shifts and data science approaches to environmental problems. Co-founder of rOpenSci and the Schmidt Center for Data Science & Environment at UC Berkeley.

[email protected] Website

Office Hours

Fridays, 9:00 AM

Zoom Link

Elena Stacy

Graduate Student Instructor

Elena is a PhD student in Agricultural and Resource Economics at UC Berkeley. Her research focuses on climate adaptation, flooding impacts, and development economics in rural contexts, with extensive field experience in India and Latin America.

[email protected]

Office Hours

Mondays, 2:00 PM - 3:30 PM

311C Wellman Hall

schedule

7 Weeks

14 sessions, 2 hours each

groups

Team-Based

Hands-on collaborative projects

school

MCS Core

Required for Master of Climate Solutions

The Agent-First Methodology

We do not memorize syntax. Instead, we learn to:

architecture

Architect

Design data flows

chat

Prompt

Direct AI implementation

fact_check

Audit

Verify outputs

rocket_launch

Deploy

Ship solutions

Four Core Modules

14 sessions over 7 weeks

analytics

Module 1

The AI-Data Analyst

Sessions 1-4

Build an interactive emissions dashboard. Learn AI-assisted coding, data cleaning, visualization, and database querying.

Streamlit DuckDB Pandas

map

Module 2

Spatial Data & Environmental Justice

Sessions 5-7

Map data center environmental impacts. Analyze geospatial patterns, demographic data, and biodiversity impacts through an environmental justice lens.

DuckDB Spatial maplibre Rasterio

description

Module 3

Working with LLMs & Unstructured Data

Sessions 8-10

Extract structured data from sustainability reports. Work with LLM APIs programmatically, structured outputs, and Model Context Protocol.

LangChain OpenRouter MCP

psychology

Module 4

The Capstone Studio

Sessions 11-14

Build a deployable MVP. Scope projects, sprint development, refine user experience, and present live demos.

MVP Demo Day Deployment

View Full Syllabus

Core Tech Stack

Industry-standard tools for rapid prototyping

terminal

VS Code / Antigravity

AI-augmented IDEs

dashboard

Streamlit

Data dashboards

storage

DuckDB

Data & spatial queries

link

LangChain

Document parsing

Questions?

For more information about this course, please reach out to the instructor.

mail Contact Instructor