NE 415 Notes ============ These draft notes support NE 415 - Introduction to Engineering Analysis. .. toctree:: :maxdepth: 2 Prologue Lesson 1 - An Introduction to Python, Spyder, and Jupyter Lesson 2 - Basic Operators and Common, Built-In Functions Lesson 3 - One-Dimensional Data Processing with NumPy and Matplotlib Lesson 4 - Two-Dimensional Data Processing with NumPy and Matplotlib Lesson 5 - Monte Carlo First Lesson 6 - Random Variables and Distributions Lesson 7 - Sampling Discrete and Continuous Random Variables Lesson 8 - Statistics and Estimators Lesson 9 - Engineering the Simulation Lesson 10 - From Balance Laws to Linear Systems Lesson 11 - Matrix and Vector Operations Lesson 12 - One Matrix, Many Scenarios Lesson 13 - Symbolic Computation with SymPy Lesson 14 - Norms, Sparse Matrices, and Iterative Methods Lesson 15 - Orthogonality, Gram-Schmidt, and Projection Lesson 16 - One Eigenpair from Iteration Lesson 17 - The Whole Spectrum at Once Lesson 18 - Eigenvalue Applications Lesson 19 - Numerical Differentiation Lesson 20 - Numerical Integration Lesson 21 - Nonlinear Equations Lesson 22 - Unconstrained Optimization Lesson 23 - Constrained Optimization Lesson 24 - Modeling Data with Least-Squares Fits Lesson 25 - Modeling Data with Interpolation Lesson 26 - Advection, Diffusion, and Reaction Models Lesson 27 - Solving ODEs Symbolically Lesson 28 - Solving IVPs with Euler's Method Lesson 29 - Solving Systems of IVPs Lesson 30 - Solving BVPs Numerically Appendix A01 - Algorithms, Flowcharts, and Pseudocode Appendix A02 - Conditional Statements and the Structure of Python Code Appendix A03 - More on Conditionals Appendix A04 - Loops in Python Appendix A05 - More on Loops Appendix A06 - Pythonic Containers Appendix A07 - Processing Text Data Appendix A08 - Basics of Functions Appendix A09 - More on Functions Appendix A10 - Recursive Functions Appendix A11 - Algorithm Complexity and Numerical Experiments Appendix A12 - Searching Appendix A13 - Sorting Appendix A14 - Modules and Packages Appendix A15 - Unit Tests and Defensive Programming