External Resources#
This book covers one path through data analytics for chemical engineers. A great deal of excellent material covers adjacent paths, and some of it is actively maintained in ways a static book cannot be.
Start with CACHE#
The CACHE Corporation Teaching Resources Center maintains two collections that are the best single starting point for this material in a chemical engineering context. Both are curated, annotated with audience and prerequisite level, and updated as things change — treat them as the superset of anything listed on this page:
Machine Learning and Artificial Intelligence — course models that can be adopted wholesale, assignment ideas with defined student deliverables, learner pathways, datasets, and guidance on generative AI and agents. Each entry is tagged with best use, audience, level, expected time, access terms, and the limitations to know about before assigning it.
Data, Statistics, and Analytics — syllabi from several departments, textbook recommendations, screencast libraries, and datasets, oriented toward the statistics and uncertainty side of the subject.
The original notes that this book was built from are listed on both pages, so you may well have arrived here from one of them.
Courses and books#
Course materials in this area are unusually open — most of the entries below include lectures, notebooks, and assignments you can work through independently.
Resource |
What it is |
Level |
|---|---|---|
Data Science and Machine Learning in Chemical Engineering (Carnegie Mellon) |
A full course: syllabus, lectures, class exercises, 13 homework sets, and a course project |
Intermediate |
Machine Learning for Engineers (APMonitor / BYU) |
Engineering-first ML course with Python notebooks, projects, and case studies; code MIT licensed |
Introductory to intermediate |
Data-Driven Engineering (APMonitor / BYU) |
Sensor data, cleaning, statistics, visualization, time series, and uncertainty, with a group project |
Introductory to intermediate |
A Jupyter Book with exercises on properties, vapor–liquid equilibrium, CSTR models, control, and PCA |
Intermediate |
|
Companion materials to the book: slides, scripts, worked examples, and data files |
Intermediate |
|
Advanced Mathematics and Computation for Chemical Engineers (Wisconsin) |
Tutorials with Colab links on neural networks, graph neural networks, topological data analysis, and t-SNE |
Advanced |
AI for Chemical Engineers (Buffalo) |
Slides and tutorials on fault detection, materials, optimization, and generative models; CC BY 4.0 |
Intermediate to advanced |
Data-Driven Science and Engineering (Brunton and Kutz) |
Videos, exercises, and code on dynamics, control, and reduced-order models. Companion materials open; textbook sold separately |
Advanced undergraduate to graduate |
Interactive text with Colab notebooks on representations, graph neural networks, equivariance, and generative models; CC BY-NC 3.0 |
Advanced |
|
Digital book on physical constraints, differentiable simulation, and surrogates. A GPU helps |
Advanced |
|
Physics-Informed Machine Learning (Washington) |
Slides and code for heat transfer, reaction discovery, and manufacturing applications |
Upper undergraduate to graduate |
PyCSE (Kitchin) |
Scientific Python for engineers, from the ground up. Strongly recommended if you are still finding your footing in Python |
Introductory |
Tools and tutorials#
Resource |
Use |
|---|---|
A thorough, exercise-driven tour of the library this book uses most |
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Regression, classification, splitting, and evaluation from first principles |
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Discovering governing equations from dynamic data |
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Surrogate models inside flowsheet optimization, from DOE and NETL |
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Turning a finished model or analysis into a small interactive application |
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LearnChE statistics screencasts (Colorado Boulder) |
Sixty-plus short videos on statistics topics, including software tutorials |
Interactive demonstrations of distributions and statistical concepts |
Generative AI, language models, and agents#
Accessing Data with AI Tools covers the practical use of these tools for getting at data. The resources below go deeper into how they work and how to build with them.
Resource |
What it covers |
|---|---|
Transformers, tokenizers, fine-tuning, and reasoning models |
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Function calling, retrieval, frameworks, observability, and evaluation |
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Chaining, routing, orchestration, and evaluator loops. Vendor-authored — pair it with your own testing |
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Building a language model end to end. Assumes strong ML and systems background |
|
Using LLMs in Scientific Research (Carnegie Mellon) |
Literature work, data analysis, coding, and keeping work repeatable |
Inspect AI (UK AI Security Institute) |
Building fixed test sets and clear scoring for model and agent evaluation |
For instructors deciding on course policy, Incorporating Generative AI in the Chemical Engineering Classroom (written for CACHE) and Cornell Engineering’s guidance both give concrete, adaptable starting points.
Using any of this responsibly#
The failure modes here are not exotic; they are the ordinary ones, and they are easy to avoid on purpose and hard to avoid by accident.
Record where the data came from. Source, license, units, missing-value convention, how it was sampled, what you changed, and the measurement uncertainty if it is stated. This costs ten minutes at the start and saves the entire analysis later.
Match the validation split to the real use. Keep related times, batches, and equipment out of both training and test sets. If the model will be applied to future data, validate on future data.
Apply engineering checks a metric will not catch. Do the predictions conserve mass and energy? Are the units right? Do limiting cases behave? What happens outside the training range? A model can have excellent cross-validated error and still predict negative concentrations.
Report how it fails, not only how well it works. Uncertainty, failure cases, drift over time, and the conditions under which the model should not be used.
Verify what a language model tells you. Check the calculations, the code, the quotations, and the sources. Confident phrasing is not evidence, and these tools are most persuasive exactly where they are least reliable — plausible-looking citations, and algebra that is formatted correctly but wrong.
Constrain agents that take actions. Limit tools and permissions, protect private data, log what was done, set cost and time limits, and require human approval for anything affecting safety, equipment, or records.
For a formal framework, the NIST AI Risk Management Framework Generative AI Profile sets out risk registers, test plans, and source records in a way that maps onto engineering practice.
Note
External links were last reviewed in August 2026. The CACHE pages linked at the top of this page are maintained continuously; if something here has gone stale, check there first. If you find a broken link, please open an issue.