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Data Analytics for Chemical Engineers
Introduction
Numerical Methods
Python Basics
Linear Algebra
Linear Regression
Numerical Optimization
Nonlinear Parameter Estimation
Regression
Non-parametric Models
Model Validation
Complexity Optimization
High-dimensional Data
High-dimensional Regression
Nonlinear Feature Engineering
Classification
Classification Basics
Generalized Linear Models
Alternate Classification Methods
High-dimensional Classification
Data Management
Data Organization
Online Data Access and APIs
Accessing Data with AI Tools
Complex Structured Data
Exploratory Data Analysis
High-Dimensional Data
Dimensionality Reduction
Clustering
Generative Models
Advanced Topics
Time Series Basics
Time Series Models
Neural Network Basics
Neural Network Architectures
Problem Sets
Module 1 — Numerical Methods
1.1 - Python Basics - Water Properties
1.2 - Linear Algebra - Steam Methane Reforming
1.3 - Linear Regression - Methanol IR Spectrum
1.4 - Numerical Optimization - CO2 Adsorption Isotherm
1.5 - Nonlinear Parameter Estimation - Isotherm Uncertainty
Module 2 — Regression
2.1 - Non-parametric Models - Refrigerant VLE Azeotrope
2.2 - Model Validation - Henry's Law Constants
2.3 - Complexity Optimization - Coffee NMR Spectrum
2.4 - High-dimensional Data - Solvent Vapor IR Mixtures
2.5 - High-dimensional Regression - Solvent Vapor IR Calibration
Module 3 — Classification
3.1 - Classification Basics - Flammability Testing
3.2 - Generalized Linear Models - Flammability Envelopes
3.3 - Alternate Classification Methods - Aqueous Solubility
3.4 - High-dimensional Classification - Membrane Database Audit
Module 5 — Exploratory Data Analysis
5.1 - High-Dimensional Data - Tennessee Eastman Sensors
5.2 - Dimensionality Reduction - Tennessee Eastman Monitoring
5.3 - Clustering - Carbon XPS Spectra
5.4 - Generative Models - XPS Batch Screening
Datasets and Resources
Datasets
Project Ideas
External Resources
Index