Problem Sets

One graded problem set per chapter. Each is worth 100 points, split across three parts that build on each other:

• Part A — Skill Checks (30 pts): short answers, auto-graded, unlimited attempts.

• Part B — Visualization (35 pts): plots and interpretation, peer graded.

• Part C — Open Ended (35 pts): one synthesis problem, peer graded.

Note

Each problem set is a standalone Jupyter notebook. The zip link below gives you the notebook and its data in a folder that runs as-is; the Download badge at the top of any problem page gives you the notebook on its own. The cells marked # YOUR CODE HERE are deliberately not executed when the book is built, so you receive them blank and ready to fill in.

Solutions are not published here.

Numerical Methods

• 1.1 - Python Basics - Water Properties (accompanies Python Basics) · zip

• 1.2 - Linear Algebra - Steam Methane Reforming (accompanies Linear Algebra) · zip

• 1.3 - Linear Regression - Methanol IR Spectrum (accompanies Linear Regression) · zip

• 1.4 - Numerical Optimization - CO2 Adsorption Isotherm (accompanies Numerical Optimization) · zip

• 1.5 - Nonlinear Parameter Estimation - Isotherm Uncertainty (accompanies Nonlinear Parameter Estimation) · zip

Regression

• 2.1 - Non-parametric Models - Refrigerant VLE Azeotrope (accompanies Non-parametric Models) · zip

• 2.2 - Model Validation - Henry’s Law Constants (accompanies Model Validation) · zip

• 2.3 - Complexity Optimization - Coffee NMR Spectrum (accompanies Complexity Optimization) · zip

• 2.4 - High-dimensional Data - Solvent Vapor IR Mixtures (accompanies High-dimensional Data) · zip

• 2.5 - High-dimensional Regression - Solvent Vapor IR Calibration (accompanies High-dimensional Regression) · zip

Classification

• 3.1 - Classification Basics - Flammability Testing (accompanies Classification Basics) · zip

• 3.2 - Generalized Linear Models - Flammability Envelopes (accompanies Generalized Linear Models) · zip

• 3.3 - Alternate Classification Methods - Aqueous Solubility (accompanies Alternate Classification Methods) · zip

• 3.4 - High-dimensional Classification - Membrane Database Audit (accompanies High-dimensional Classification) · zip

Exploratory Data Analysis

• 5.1 - High-Dimensional Data - Tennessee Eastman Sensors (accompanies High-Dimensional Data) · zip

• 5.2 - Dimensionality Reduction - Tennessee Eastman Monitoring (accompanies Dimensionality Reduction) · zip

• 5.3 - Clustering - Carbon XPS Spectra (accompanies Clustering) · zip

• 5.4 - Generative Models - XPS Batch Screening (accompanies Generative Models) · zip