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Introduction to the Modeling and Analysis of Complex Systems
Hiroki Sayama · Open SUNY · 2015
Introduction to the Modeling and Analysis of Complex Systems introduces students to mathematical/computational modeling and analysis developed in the emerging interdisciplinary field of Complex Systems Science. Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves. This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Most of these topics are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis. This unique approach provides a comprehensive view of related concepts and techniques, and allows readers and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example.
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Principles of Data Science
Dr. Shaun V. Ault; Dr. Soohyun Nam Liao; Larry Musolino; Wisam Bukaita · OpenStax, Rice University
Principles of Data Science is intended to support one- or two-semester courses in data science. It is appropriate for data science majors and minors as well as students concentrating in business, finance, health care, engineering, the sciences, and a number of other fields where data science has become critically important. The authors have included a diverse mix of scenarios, examples, and data types for analysis and discussion purposes. These include both fictional contexts and real-world sources, such as the Federal Reserve Economic Database and Nasdaq. Data sets focus on a range of topics: business, science, social sciences. Applications include healthcare, physical sciences, demographics, policy, and finance. Data ethics and the emergence of artificial intelligence are covered deeply – both in their own chapters and as consistent threads throughout the course material. The authors and contributors have developed rich in-chapter example problems and extensive practice exercises that encourage students to apply concepts in a variety of situations. Technical illustrations and Python code support and supplement the principles and theory. The text also includes direct links to downloadable data sets and Python code, as well as guidance on how to use them.
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Programming Fundamentals - A Modular Structured Approach, 2ed.
Kenneth Leroy Busbee and Dave Braunschweig · LibreTexts
In the contents: 1.15: Python Examples · 2.03: Python Examples · 3.15: Python Examples
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Quantum Mechanics III
Y. D. Chong · Nanyang Technological University
An advanced course on quantum mechanics, covering scattering and resonances, multi-particle quantum mechanics, and the basics of quantum field theory. Python source files for the numerical examples are available on Github (GPLv3).
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LibreTexts Construction Guide
Delmar Larsen · University of California, Davis
In the contents: 5.2.04: How to Incorporate Data Files in Your Code Blocks- For Python
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LibreTexts Construction Guide 3e
LibreTexts
In the contents: 14.2.04: How to Incorporate Data Files in Your Code Blocks- For Python · 18.02: Python Binder Examples · 18.2.01: Python Examples
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Computational Physics
Y. D. Chong · Nanyang Technological University
An introductory course in computational physics for upper-level undergraduates. Topics covered include numerical linear algebra, eigenvalue problems, sparse matrix problems, numerical integration and initial-value problems, Fourier transforms, and Monte Carlo simulations. Programming examples are based on Scientific Python.
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Introduction to Data Analytics
Felix Amoruwa · De Anza College
In the contents: 7.09: Practical Examples with Python- Code and Outputs · 8.02: Open-Source Visualization Libraries (Python) · 12.01: Python for Data Analytics
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A Primer for Computational Biology
Shawn T. O’Neil · Oregon State University
In the contents: 02: Programming in Python · 2.06: Python Functions
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Making LibreTexts Interactive with Binder: A Practical Guide
Carl Greco · Arkansas Tech University
In the contents: 02: Python Binder Examples · 2.01: Python Examples
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USD CHEM 220 Fall 2022
Eleanor I. Gillette · University of San Diego
In the contents: 2.05: Python Basics for Analytical Chemists
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Matrix Algebra with Computational Applications
Dirk Colbry · Michigan State University
In the contents: 1.2: Review Python Packages · 4.3: Vectors in Python · 07: 04 Pre-Class Assignment - Python Linear Algebra Packages
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Spring 2024: Physical Computing Class
LibreTexts
In the contents: 04: Python Data Types · 08: Python Functions Packages and Formatting · 9.23: 3-Python and Libcamera
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Cheminformatics
LibreTexts
In the contents: 1.04: Installing Python · 1.10: Python Assignment 1 · 2.07: Python Assignment
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Introductory Physics: Building Models to Describe Our World
Ryan D. Martin, Emma Neary, Joshua Rinaldo, and Olivia Woodman · Queen's University
In the contents: 28: The Python Programming Language · 28.04: The QExpy python package for experimental physics
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Construction Guide for LibreTexts 2e
LibreTexts · LibreTexts
In the contents: 11.2.04: How to Incorporate Data Files in Your Code Blocks- For Python
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Introductory Physics: Building Models to Describe Our World
Ryan D. Martin, Emma Neary, Joshua Rinaldo, and Olivia Woodman · Queen's University
In the contents: 28: The Python Programming Language · 28.04: The QExpy python package for experimental physics
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MATH 2200: Calculus for Scientists II
Pamini Thangarajah · Mount Royal University
In the contents: Solutions and Visuals using Python
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Principles of Data Science
OpenStax · LibreTexts
In the contents: 1.05: Data Science with Python · 9.04: Geospatial and Heatmap Data Visualization Using Python · 9.05: Multivariate and Network Data Visualization Using Python
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The Crystal Ball - Instruction Manual I: Introduction to Data Science
Stephen Davies · University of Mary Washington
In the contents: 3.04: The Three Kinds in Python · 08: Arrays in Python (1 of 2) · 09: Arrays in Python (2 of 2)