cbe-xx258

Chapter 1.0 Python Primer

• 1.0.1 Why Python?
• 1.0.2 Chapter Learning Objectives
• 1.0.3 Activities
• Markdown Figures
  • image1
  • image2

1.1 Welcome to Jupyter Notebooks and Vocareum

• 1.1.1 Quick Python Introduction
  • 1.1.1.1 Variables
  • 1.1.1.2 Execution Order
• 1.1.2 Vocareum Autograder
• 1.1.3 Gradescope
  • 1.1.3.1 Handwritten Work
  • 1.1.3.2 Notebooks

1.2 Learning Python Basics

• 1.2.1 Learning Objectives
• 1.2.2 Hello World and Interactive Sessions
  • 1.2.2.1 Command Line Mode
• 1.2.3 Comments
• 1.2.4 Errors
  • 1.2.4.1 "Good" Bugs
  • 1.2.4.2 "Bad" Bugs
• 1.2.5 Indentation
• 1.2.6 Input
• 1.2.7 Variables
  • 1.2.7.1 Integers
  • 1.2.7.2 Floating Point Numbers
  • 1.2.7.3 Built-in Mathematical Functions
• 1.2.8 Strings and Overloading

1.3 Flow Control and Pseudocode

• 1.3.1 Learning Objectives
• 1.3.2 If Statements
• 1.3.3 Iteration with While Loops
• 1.3.4 For Loops
• 1.3.5 Pseudocode
• 1.3.6 Estimate $\pi$ by throwing darts
  • 1.3.6.1 Governing Formula
  • 1.3.6.2 Pseudocode
    • 1.3.6.2.1 Python Implementation
    • 1.3.6.2.2 Python
  • 1.3.6.3 Using Help to Learn More About a Function
  • 1.3.6.4 Another Solution Approach - With Plots
• 1.3.7 TODO: Add another pseudocode example

1.4 Functions, Scoping, and Other Fun Stuff

• 1.4.1 Learning Objectives
• 1.4.2 Functions
  • 1.4.2.1 Calling functions
  • 1.4.2.2 Return Values
  • 1.4.2.3 Docstrings and Help
• 1.4.3 Scope
• 1.4.4 Recursion
• 1.4.5 Modules
• 1.4.6 Files
• 1.4.7 Example: High/Low Guess My Number Game
  • 1.4.7.1 Instructions
  • 1.4.7.2 Pseudocode
  • 1.4.7.3 Python Implementation
  • 1.4.7.4 Testing

1.5 List, Dictionaries, and Enumeration

• 1.5.1 Learning Objectives
• 1.5.2 Lists
• 1.5.3 Dictionaries
  • 1.5.3.1 Example: Shopping List
  • 1.5.3.2 Another Example: Chemical Symbols and Element Names
  • 1.5.3.3 Dictionary of Dictionaries
• 1.5.4 Enumerate
  • 1.5.4.1 Lists
  • 1.5.4.2 Dictionaries

1.6 Linear Algebra with Numpy and Scipy

• 1.6.1 Learning Objectives
• 1.6.2 NumPy Arrays
  • 1.6.2.1 Getting Started
  • 1.6.2.2 Creating Arrays in Neat Ways
  • 1.6.2.3 Operations on Arrays
  • 1.6.2.4 Universal Functions
  • 1.6.2.5 Copying Arrays
  • 1.6.2.6 Indexing, Slicing, and Iterating
  • 1.6.2.7 Complex Numbers
  • 1.6.2.8 Integrating Everything Together

1.7 Visualization with matplotlib

• 1.7.1 Learning Objectives
• 1.7.2 Matplotlib Basics
  • 1.7.2.1 3b-i. Customizing Plots
  • 1.7.2.2 3b-ii. Plotting multiple lines
• 1.7.3 Publication Quality Figures

1.8 Manipulating Data with Pandas

• 1.8.1 Learning Objectives
• 1.8.2 Working with Data Using Pandas
  • 1.8.2.1 Loading and Inspecting Data
  • 1.8.2.2 Computing Summary Statistics
  • 1.8.2.3 Combining Pandas and Matplotlib
• 1.8.3 Investment Strategies
  • 1.8.3.1 Getting Started
  • 1.8.3.2 Portfolio Calculator
• Markdown Links
  • the publisher
  • 10 minutes to pandas

1.9 Functions as Arguments

• 1.9.1 Learning Objectives
• 1.9.2 Functions passed to Functions
  • 1.9.2.1 Motivating Example: Midpoint Integration
  • 1.9.2.2 Convergence Analysis
  • 1.9.2.3 A Fancier Integration Function
• 1.9.3 Lambda Functions
  • 1.9.3.1 Example: Lambda Functions and Midpoint Integration
  • 1.9.3.2 4c-ii. Extension to Two Dimensional Functions

1.10 Testing and Debugging in Python

- [1.10.1 Learning Objectives](https://ndcbe.github.io/cbe-xx258/01.10-Testing-and-Debugging.html#1.10.1-Learning-Objectives)

• 1.10.1 Testing Your Code
  • 1.10.1.1 Test 0: Takes Inputs as Advertised?
  • 1.10.1.2 Test 1: Initial Condition
  • 1.10.1.3 Test 2: Asymptotics
  • 1.10.1.4 Test 3: Check a Few Inputs
  • 1.10.1 Debugging is a Mindset
  • 1.10.2 Test 2
  • 1.10.3 Test 3

Chapter 2.0 Linear Algebra

Chapter 3.0 Numeric Linear Algebra

Chapter 4.0 Algorithm Building Blocks

Chapter 5.0 Nonlinear Systems of Equations

Chapter 6.0 Computational Optimization

Chapter 7.0 Numeric Integration

Chapter 8.0 Descriptive Statistics and Data Visualization

Chapter 9.0 Probability Theory

Chapter 10.0 Common Probability Distributions

Chapter 11.0 Uncertainty and Error Propagation

Chapter 12.0 Statistical Inference

Chapter 13.0 Regression

Chapter 14.0 Advanced Regression

Chapter 15.0 Design of Experiments

Chapter 16.0 Bayesian Statistics