Table of Contents for Digital Resources

Workbook 01

• 1: Introduction to the Kontinua Sequence
• 2: Matter and Energy
• 3: Atomic and Molecular Mass
• 4: Work and Energy
• 5: Units and Conversions
• 6: Simple Machines
• 7: Buoyancy
• 8: Heat

Workbook 02

• 1: Cognitive Biases 1
• 2: Friction
• 3: The Greek Alphabet
• 4: Basic Statistics
• 5: Basic Statistics in Spreadsheets

Workbook 03

• 1: Introduction to Electricity
• 2: DC Circuit Analysis
• 3: Charge
• 4: Fertilizer
• 5: Concrete
• 6: Metals

Workbook 04

• 1: Angles
• 2: Introduction to Triangles
• 3: Pythagorean Theorem
• 4: Congruence
• 5: Parallel and Perpendicular
• 6: Circles

Workbook 05

• 1: Functions and Their Graphs
• 2: Volumes of Common Solids
• 3: Conic Sections
• 4: Manufacturing

Workbook 06

• 1: Falling Bodies
• 2: Solving Quadratics
• 3: Complex Numbers
• 4: Introduction to Sequences

Workbook 07

• 1: Vectors
• 2: Momentum
• 3: The Dot Product
• 4: Boats
• 5: Sailboats
• 6: Airplanes
• 7: Quadcopters
• 8: Helicopters

Workbook 08

• 1: Introduction to Spreadsheets
• 2: Compound Interest
• 3: Introduction to Data Visualization

Workbook 09

• 1: Atmospheric Pressure
• 2: Exponents
• 3: Exponential Decay
• 4: Logarithms

Workbook 10

• 1: Trigometric Functions
• 2: Inverse Trigonometric Functions
• 3: Trigonometric Identities
• 4: Transforming Functions
• 5: Polar Coordinates

Workbook 11

• 1: Sound
• 2: Alternating Current
• 3: Electric Motor
• 4: Drag
• 5: Vector-valued Functions
• 6: Circular Motion
• 7: Orbits
• 8: Rocketry
• 9: Simulation with Vectors
• 10: Longitude and Latitude
• 11: Tides and Eclipses

Workbook 12

• 1: Electromagnetic Waves
• 2: How Cameras Work
• 3: How Eyes Work
• 4: Reflection
• 5: Refraction
• 6: Lenses
• 7: Images in Python

Workbook 13

• 1: Introduction to Polynomials
• 2: Python Lists
• 3: Adding and Subtracting Polynomials
• 4: Multiplying Polynomials
• 5: Multiplying Polynomials in Python

Workbook 14

• 1: Differentiating Polynomials
• 2: Python Classes
• 3: Common Polynomial Products
• 4: Factoring Polynomials
• 5: Partial Fractions

Workbook 15

• 1: Practice with Polynomials
• 2: Graphing Polynomials
• 3: Interpolating with Polynomials

Workbook 16

• 1: Fabrication
• 2: Limits
• 3: Rational Functions

Workbook 17

• 1: Differentiation
• 2: Derivatives
• 3: Rules for Finding Derivatives
• 4: First and Second Derivatives and the Shape of a Function
• 5: Optimization

Workbook 18

• 1: Implicit Differentiation
• 2: Related Rates
• 3: Multivariate Functions
• 4: Partial Derivatives and Gradients

Workbook 19

• 1: Introduction to Linear Algebra
• 2: Vectors and Matrices
• 3: Vector Spans and Independence
• 4: Matrices

Workbook 20

• 1: Projections
• 2: The Gram-Schmidt Process
• 3: Eigenvectors and Eigenvalues
• 4: Singular Value Decomposition
• 5: Tackling Difficult Problems: Positive Semidefinite Matrices

Workbook 21

• 1: Data Tables and pandas
• 2: Data tables in SQL
• 3: Representing Natural Numbers

Workbook 22

• 1: Making Web Requests with HTTP
• 2: Using and Creating APIs
• 3: Data Compression and Decompression
• 4: Dealing with JSON and XML
• 5: HTML
• 6: Introduction to Text
• 7: Stop Words
• 8: Stemming and Lemmatization
• 9: Alphabets and Accents

Workbook 23

• 1: Making Plots with matplotlib
• 2: Geographical Data
• 3: Geocoding and Reverse Geocoding
• 4: Making a Map

Workbook 24

• 1: Introduction to Discrete Probability
• 2: Beginning Combinatorics
• 3: Permutations and Sorting

Workbook 25

• 1: Conditional Probability
• 2: Bayes' Theorem

Workbook 26

• 1: Antiderivatives
• 2: Riemann Sums
• 3: Definite Integrals
• 4: The Fundamental Theorem of Calculus
• 5: Arc Lengths
• 6: Continuous Probability Distributions
• 7: The Physics of Gases
• 8: Kinetic Energy and Temperature of a Gas
• 9: Phases of Matter
• 10: The Piston Engine

Workbook 27

• 1: u-Substitution
• 2: Calculus with Polar Coordinates
• 3: Differential Equations
• 4: Slope Fields
• 5: Euler's Method
• 6: Sequences in Calculus
• 7: Series
• 8: Convergence Tests for Series
• 9: Power Series

Workbook 28

• 1: Population Proportion Statistics
• 2: The Normal Distribution
• 3: Change of Variables
• 4: Poisson and Exponential Probability Distributions

Workbook 29

• 1: Volumes with Integrals
• 2: Double Integrals over Rectangular Regions
• 3: Double Integrals Over Non-Rectangular Regions
• 4: Applications of Double Integrals
• 5: Multivariate Distributions
• 6: The Multivariate Normal Distribution

Workbook 30

• 1: Sets and Logic
• 2: Linked Lists
• 3: Trees
• 4: Searching Trees
• 5: Hash Tables
• 6: Sorting Algorithms

Workbook 31

• 1: Introduction to Graphs
• 2: Dijkstra's Algorithm
• 3: Binary Search
• 4: Other Graph Algorithms
• 5: Bayesian Networks

Workbook 32

• 1: Introduction to Classification and Regression
• 2: Simple Linear Regression
• 3: Simple Logistic Regression
• 4: Standardizing Data
• 5: One-Hot Encoding
• 6: Multiple Logistic Regression

Workbook 33

• 1: The Training/Validation/Testing Process
• 2: Evaluating Classification Systems
• 3: Evaluation Binary Classifiers
• 4: The k-Nearest Neighbor Classifier
• 5: Bayesian Classifiers

Workbook 34

• 1: Evaluating the Fit of a Linear Regression Model
• 2: Linear Regression and Gradient Descent
• 3: Generalized Linear Models
• 4: Link Functions

Workbook 35

• 1: Decision Trees for Classification
• 2: Bagging and Random Forests
• 3: Boosting
• 4: Clustering using k-Means

Workbook 36

• 1: Neural Nets for Regression
• 2: Neural Networks for Classification
• 3: Deep Learning