Syllabus | Module 1

Day 1 | Monday, January 27, 2025

Course Introduction (45 min)

• Welcome
• Introductions
• What is Data Science?
• What is Earth System Data Science in the Cloud?
• Course Goals and Objectives
• Module Goals and Objectives
• Course Logistics

Introduction to Command Line (60 min)

• What is Bash?
• What is an Environment?
• Navigating your Environment
• Manipulating your Environment
• Command Line Text Editors

Introduction to Python (60 min)

• Launching Python
• Versions
• Python as a Calculator
• Variable Assignment
• Beginning Data Structures

Lunch & Learn (60 min)

• Introductions
• Personal Goals
• Ask Me Anything

Day 2 | Tuesday January 28, 2025

Introduction to Git (60 min)

• Version Control
• Git Architecture
• Add, Commit, Push

Meet your Programming Assistant (60 min)

• Intro to LLMs
• Code Completion
• Best Practices for Communicating:
  • Generation
  • Correction
  • Documentation
  • Translation

Learning about Learning (30 min)

How do we learn programming languages?

• Read, Evaluate, Print, and Loop (REPL)
• Iteration
• Random Contextual Interference
• Programming Assistants

Programming in Python (30 min)

• Leveraging REPL for fast iteration
• Best Practices for Script Construction
• Comments and In-line Documentation
• Beginnings of Workflow Management

Lunch & Learn (60 min)

• Progress Check-In
• Questions

Fundamentals of Computing (60 min)

• What is a programming language?
  • Types of Programming languages
  • Compiled and Scripted
    • Interpreters
  • Advantages and Disadvantages
  • The Programming Language Landscape
• What is a Computer?
  • Storage
  • Memory (RAM)
  • Compute
  • Networking
• Python
  • History
  • Advantages and Disadvantages
  • Parallelization (Thread Lock)
  • Support for Different Data Types
  • Support for Different Types of Analysis

Programming Paradigms (60 min)

• Objects
• Functions!
• Classes!
• Object Oriented vs Functional Programming

Day 3 | Wednesday, January 29, 2025

Collaborative Git I (60 min)

• GitLab & Git
• Cloning, pulling, and pushing
• Branching, Merging, and Issues
• Communication & Project Structure

Leveraging LLMs (60 min)

• Large Language Model (LLM) Landscape
• Accessing and using LLMs
• Learning with and from LLMs
• Prompt Engineering

Dependency Management in Python (30 min)

• Install Libraries/Packages
• Importing Libraries/Packages
• Managing Libraries/Packages
• Environment Management
• Python Library Ecosystem

Data Types | Python (30 min)

• Scalars
• Vectors
• Arrays/Matrices
• Data Frames
• Indexing

Lunch & Learn (60 min)

• Technical Session: Why Scientists say what they do...

Control Structures (60 min)

• Loops
• If/Then
• Case
• Try/Except
• Decorators

Day 4 | Thursday, January 30, 2025

Introduction to Production Machine Learning (60 min)

• Models
• APIs
• Deployment

Collaborative Git I Continued (60 min)

• GitLab & Git
• Cloning, pulling, and pushing
• Branching, Merging, and Issues
• Communication & Project Structure

Team Kickoff (30 min)

• Introductions
• Research Theme Definition
• Personal and Project goals

Working in Teams (60 min)

• Communication
• Organization
• Roles
• ESDS Team Projects

Lunch & Learn (60 min)

• Team Project Discussion

LLMs, Data, and Agents (60 min)

• LLM Context
• Data Sources, Projects, and Retrieval Augmented Generation
• Building and Using Agents

Beginning a Project (30 min)

• Defining a Project
• Choosing a Language
• Finding Data
• Accessing Data
• Introduction to Data Formats

Team Time (60 min)

• Meet your team
• Beginning Team Project Discussions

Day 5 | Friday, January 31, 2025

Collaborative Git II (60 min)

• GitLab & Git
• Cloning, pulling, and pushing
• Branching, Merging, and Issues
• Communication & Project Structure

Foundations of Parallel Computing (30 min)

• What is parallel computing
• Units of parallelization
• MapReduce
• Why Map Reduce changed the world
• The two key types of Parallel Computing

Closing Team Exercise (60 min)

• Closing Exercise
• Module Wrap Up
• Next Steps