Data Science and Machine Learning in Chemical Engineering

Data Science and Machine Learning in Chemical Engineering#

06-642 · Spring 2026 · Carnegie Mellon University

Welcome to the course! This half-semester course covers practical applications of data science and machine learning techniques to problems in chemical engineering and related sciences.

Course Goals#

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

  1. Manipulate data using NumPy and Pandas for analysis and visualization

  2. Build predictive models using scikit-learn for regression and classification

  3. Apply advanced techniques including ensemble methods, clustering, and dimensionality reduction

  4. Quantify uncertainty in model predictions using pycse and Gaussian processes

  5. Interpret models to understand what drives predictions

  6. Apply these skills to real chemical engineering problems

Course Structure#

The course consists of 12 lectures covering:

Module

Topic

00

Introduction & Python Environment

01

NumPy Fundamentals

02

Pandas Introduction

03

Intermediate Pandas

04

Dimensionality Reduction

05

Linear Regression

06

Regularization & Model Selection

07

Nonlinear Methods

08

Ensemble Methods

09

Clustering

10

Uncertainty Quantification

11

Model Interpretability

Plus optional lectures on databases, deep learning, symbolic regression, and LLMs.

Prerequisites#

  • Basic Python programming

  • Undergraduate mathematics (linear algebra, calculus, statistics)

  • No prior machine learning experience required

Interactive Learning#

This course includes an interactive learning system to help reinforce concepts:

Data Academy Honors#

Earn badges and ranks as you progress:

  • 5 Badges: Data Wrangler, Pattern Seeker, Model Builder, Ensemble Master, Uncertainty Expert

  • 4 Ranks: Data Apprentice → Data Analyst → Data Scientist → Senior Data Scientist

See Data Academy Honors for details.

Games & Activities#

  • Trivia (/trivia): Test your knowledge with character-hosted questions

  • Adventure (/adventure): “The Data Detective Agency” - solve ChemE data mysteries

  • Flashcards (/flashcards): Spaced repetition for key concepts

  • Puzzles (/puzzle): Metric matching, confusion matrices, code completion

  • Scavenger Hunts (/hunt): Explore datasets, documentation, and papers

Course Characters#

Meet your guides including Nadia Null, Reggie Regression, Val Validation, and more! See characters.md.

Quick Reference#

The reference sheet provides concise summaries of pandas, sklearn, metrics, and common patterns.

Repository Setup#

Initial Setup#

# Clone the course repository
git clone <course-repo-url>
cd dsmles

# Install notebook merge tool (handles Jupyter notebook conflicts)
pip install nbdime
nbdime config-git --enable --global

# Create your working branch (keep main clean for updates)
git checkout -b my-work

Getting Course Updates#

When the instructor announces updates:

# Switch to main and pull updates
git checkout main
git pull origin main

# Return to your working branch
git checkout my-work

# Optionally merge updates into your branch
git merge main

If you encounter notebook merge conflicts, nbdime provides a visual merge tool:

git mergetool --tool=nbdime

Why This Workflow?#

  • main branch stays clean and always pulls without conflicts

  • your branch contains all your work, notes, and completed exercises

  • you control when to bring in updates

  • nbdime handles notebook merges intelligently when needed

Getting Started#

  1. Review the Syllabus for course policies

  2. Set up your Python environment (see Module 00: Introduction)

  3. Clone the repo and create your working branch (see above)

  4. Complete assignments as they are released

  5. Try /trivia to test your knowledge as you learn!

Let’s begin!

Contents