- file: intro

- file: 01/what-is-data-science
  numbered: 2
  sections:
    - file: 01/1/ch1_intro
      sections:
        - file: 01/1/1/computational-tools
        - file: 01/1/2/statistical-techniques
    - file: 01/2/why-data-science
    - file: 01/3/Plotting_the_Classics
      sections:
        - file: 01/3/1/Literary_Characters
        - file: 01/3/2/Another_Kind_Of_Character

- file: 02/causality-and-experiments
  numbered: 2
  sections:
    - file: 02/1/observation-and-visualization-john-snow-and-the-broad-street-pump
    - file: 02/4/randomization
    - file: 02/3/establishing-causality
    - file: 02/2/snow-s-grand-experiment
    - file: 02/5/endnote

- file: 03/programming-in-python
  numbered: 2
  sections:
    - file: 03/1/Expressions
    - file: 03/2/Names
      sections:
        - file: 03/2/1/Growth
    - file: 03/3/Calls
    - file: 03/4/Introduction_to_Tables

- file: 04/Data_Types
  numbered: 2
  sections:
    - file: 04/1/Numbers
    - file: 04/2/Strings
      sections:
        - file: 04/2/1/String_Methods
    - file: 04/3/Comparison

- file: 05/Sequences
  numbered: 2
  sections:
    - file: 05/1/Arrays
    - file: 05/2/Ranges
    - file: 05/3/More_on_Arrays

- file: 06/Tables
  numbered: 2
  sections:
    - file: 06/1/Sorting_Rows
    - file: 06/2/Selecting_Rows
    - file: 06/3/Example_Trends_in_the_Population_of_the_United_States
    - file: 06/4/Example_Gender_Ratio_in_the_US_Population

- file: 07/Visualization
  numbered: 2
  sections:
    - file: 07/1/Visualizing_Categorical_Distributions
    - file: 07/2/Visualizing_Numerical_Distributions
    - file: 07/3/Overlaid_Graphs

- file: 08/Functions_and_Tables
  numbered: 2
  sections:
    - file: 08/1/Applying_a_Function_to_a_Column
    - file: 08/2/Classifying_by_One_Variable
    - file: 08/3/Cross-Classifying_by_More_than_One_Variable
    - file: 08/4/Joining_Tables_by_Columns
    - file: 08/5/Bike_Sharing_in_the_Bay_Area

- file: 09/Randomness
  numbered: 2
  sections:
    - file: 09/1/Conditional_Statements
    - file: 09/2/Iteration
    - file: 09/3/Simulation
    - file: 09/4/Monty_Hall_Problem
    - file: 09/5/Finding_Probabilities

- file: 10/Sampling_and_Empirical_Distributions
  numbered: 2
  sections:
    - file: 10/1/Empirical_Distributions
    - file: 10/2/Sampling_from_a_Population
    - file: 10/3/Empirical_Distribution_of_a_Statistic

- file: 11/Testing_Hypotheses
  numbered: 2
  sections:
    - file: 11/1/Assessing_Models
    - file: 11/2/Multiple_Categories
    - file: 11/3/Decisions_and_Uncertainty
    - file: 11/4/Error_Probabilities

- file: 12/Comparing_Two_Samples
  numbered: 2
  sections:
    - file: 12/1/AB_Testing
    - file: 12/2/Deflategate
    - file: 12/3/Causality

- file: 13/Estimation
  numbered: 2
  sections:
    - file: 13/1/Percentiles
    - file: 13/2/Bootstrap
    - file: 13/3/Confidence_Intervals
    - file: 13/4/Using_Confidence_Intervals

- file: 14/Why_the_Mean_Matters
  numbered: 2
  sections:
    - file: 14/1/Properties_of_the_Mean
    - file: 14/2/Variability
    - file: 14/3/SD_and_the_Normal_Curve
    - file: 14/4/Central_Limit_Theorem
    - file: 14/5/Variability_of_the_Sample_Mean
    - file: 14/6/Choosing_a_Sample_Size

- file: 15/Prediction
  numbered: 2
  sections:
    - file: 15/1/Correlation
    - file: 15/2/Regression_Line
    - file: 15/3/Method_of_Least_Squares
    - file: 15/4/Least_Squares_Regression
    - file: 15/5/Visual_Diagnostics
    - file: 15/6/Numerical_Diagnostics

- file: 16/Inference_for_Regression
  numbered: 2
  sections:
    - file: 16/1/Regression_Model
    - file: 16/2/Inference_for_the_True_Slope
    - file: 16/3/Prediction_Intervals

- file: 17/Classification
  numbered: 2
  sections:
    - file: 17/1/Nearest_Neighbors
    - file: 17/2/Training_and_Testing
    - file: 17/3/Rows_of_Tables
    - file: 17/4/Implementing_the_Classifier
    - file: 17/5/Accuracy_of_the_Classifier
    - file: 17/6/Multiple_Regression
  
- file: 18/Updating_Predictions
  numbered: 2
  sections:
    - file: 18/1/More_Likely_than_Not_Binary_Classifier
    - file: 18/2/Making_Decisions














