Taxonomy Distribution Report

Overview

• Total Concepts: 300
• Number of Taxonomies: 15
• Average Concepts per Taxonomy: 20.0

Distribution Summary

Category	TaxonomyID	Count	Percentage	Status
Statistics	STATS	30	10.0%	OK
Advanced Regression	ADVR	30	10.0%	OK
Visualization	VIZ	25	8.3%	OK
Regression	REGR	25	8.3%	OK
Model Evaluation	EVAL	25	8.3%	OK
Data Structures	DSTRC	20	6.7%	OK
Data Cleaning	CLEAN	20	6.7%	OK
Machine Learning	ML	20	6.7%	OK
Neural Networks	NN	20	6.7%	OK
PyTorch	TORCH	20	6.7%	OK
Foundation Concepts	FOUND	19	6.3%	OK
Python Environment	PYENV	15	5.0%	OK
NumPy Computing	NUMPY	15	5.0%	OK
Best Practices	BEST	11	3.7%	OK
Projects	PROJ	5	1.7%	Under

Visual Distribution

STATS  #####  30 ( 10.0%)
ADVR   #####  30 ( 10.0%)
VIZ    ####  25 (  8.3%)
REGR   ####  25 (  8.3%)
EVAL   ####  25 (  8.3%)
DSTRC  ###  20 (  6.7%)
CLEAN  ###  20 (  6.7%)
ML     ###  20 (  6.7%)
NN     ###  20 (  6.7%)
TORCH  ###  20 (  6.7%)
FOUND  ###  19 (  6.3%)
PYENV  ##  15 (  5.0%)
NUMPY  ##  15 (  5.0%)
BEST   #  11 (  3.7%)
PROJ      5 (  1.7%)

Balance Analysis

No Over-Represented Categories

All categories are under the 30% threshold. Good balance!

Under-Represented Categories (<3%)

• Projects (PROJ): 5 concepts (1.7%)
• Note: Small categories are acceptable for specialized topics

Category Details

Statistics (STATS)

Count: 30 concepts (10.0%)

Concepts:

• 1. Descriptive Statistics
• 1. Mean
• 1. Median
• 1. Mode
• 1. Range
• 1. Variance
• 1. Standard Deviation
• 1. Quartiles
• 1. Percentiles
• 1. Interquartile Range
• 1. Skewness
• 1. Kurtosis
• 1. Distribution
• 1. Normal Distribution
• 1. Probability
• ...and 15 more

Advanced Regression (ADVR)

Count: 30 concepts (10.0%)

Concepts:

• 1. Multiple Linear Regression
• 1. Multiple Predictors
• 1. Multicollinearity
• 1. Variance Inflation Factor
• 1. Feature Selection
• 1. Forward Selection
• 1. Backward Elimination
• 1. Stepwise Selection
• 1. Categorical Variables
• 1. Dummy Variables
• 1. One-Hot Encoding
• 1. Interaction Terms
• 1. Polynomial Features
• 1. Feature Engineering
• 1. Feature Importance
• ...and 15 more

Visualization (VIZ)

Count: 25 concepts (8.3%)

Concepts:

• 1. Data Visualization
• 1. Matplotlib Library
• 1. Figure
• 1. Axes
• 1. Plot Function
• 1. Line Plot
• 1. Scatter Plot
• 1. Bar Chart
• 1. Histogram
• 1. Box Plot
• 1. Pie Chart
• 1. Subplot
• 1. Figure Size
• 1. Title
• 1. Axis Labels
• ...and 10 more

Regression (REGR)

Count: 25 concepts (8.3%)

Concepts:

• 1. Regression Analysis
• 1. Linear Regression
• 1. Simple Linear Regression
• 1. Regression Line
• 1. Slope
• 1. Intercept
• 1. Least Squares Method
• 1. Residuals
• 1. Sum of Squared Errors
• 1. Ordinary Least Squares
• 1. Regression Coefficients
• 1. Coefficient Interpretation
• 1. Prediction
• 1. Fitted Values
• 1. Regression Equation
• ...and 10 more

Model Evaluation (EVAL)

Count: 25 concepts (8.3%)

Concepts:

• 1. Model Performance
• 1. Training Data
• 1. Testing Data
• 1. Train Test Split
• 1. Validation Data
• 1. R-Squared
• 1. Adjusted R-Squared
• 1. Mean Squared Error
• 1. Root Mean Squared Error
• 1. Mean Absolute Error
• 1. Residual Analysis
• 1. Residual Plot
• 1. Overfitting
• 1. Underfitting
• 1. Bias
• ...and 10 more

Data Structures (DSTRC)

Count: 20 concepts (6.7%)

Concepts:

• 1. Lists
• 1. Dictionaries
• 1. Tuples
• 1. Arrays
• 1. Pandas Library
• 1. DataFrame
• 1. Series
• 1. Index
• 1. Column
• 1. Row
• 1. Data Loading
• 1. CSV Files
• 1. Read CSV
• 1. Data Inspection
• 1. Head Method
• ...and 5 more

Data Cleaning (CLEAN)

Count: 20 concepts (6.7%)

Concepts:

• 1. Missing Values
• 1. NaN
• 1. Null Detection
• 1. Dropna Method
• 1. Fillna Method
• 1. Imputation
• 1. Data Type Conversion
• 1. Duplicate Detection
• 1. Duplicate Removal
• 1. Outliers
• 1. Outlier Detection
• 1. Data Validation
• 1. String Cleaning
• 1. Column Renaming
• 1. Data Filtering
• ...and 5 more

Machine Learning (ML)

Count: 20 concepts (6.7%)

Concepts:

• 1. Machine Learning
• 1. Supervised Learning
• 1. Unsupervised Learning
• 1. Classification
• 1. Clustering
• 1. Training Process
• 1. Learning Algorithm
• 1. Model Training
• 1. Generalization
• 1. Training Error
• 1. Test Error
• 1. Prediction Error
• 1. Loss Function
• 1. Cost Function
• 1. Optimization
• ...and 5 more

Neural Networks (NN)

Count: 20 concepts (6.7%)

Concepts:

• 1. Neural Networks
• 1. Artificial Neuron
• 1. Perceptron
• 1. Activation Function
• 1. Sigmoid Function
• 1. ReLU Function
• 1. Input Layer
• 1. Hidden Layer
• 1. Output Layer
• 1. Weights
• 1. Biases
• 1. Forward Propagation
• 1. Backpropagation
• 1. Deep Learning
• 1. Network Architecture
• ...and 5 more

PyTorch (TORCH)

Count: 20 concepts (6.7%)

Concepts:

• 1. PyTorch Library
• 1. Tensors
• 1. Tensor Operations
• 1. Autograd
• 1. Automatic Differentiation
• 1. Computational Graph
• 1. Neural Network Module
• 1. Sequential Model
• 1. Linear Layer
• 1. Loss Functions PyTorch
• 1. Optimizer
• 1. SGD Optimizer
• 1. Adam Optimizer
• 1. Training Loop
• 1. Model Evaluation PyTorch
• ...and 5 more

Foundation Concepts (FOUND)

Count: 19 concepts (6.3%)

Concepts:

• 1. Data Science
• 1. Python Programming
• 1. Data
• 1. Variables
• 1. Data Types
• 1. Numerical Data
• 1. Categorical Data
• 1. Ordinal Data
• 1. Nominal Data
• 1. Measurement Scales
• 1. Independent Variable
• 1. Dependent Variable
• 1. Dataset
• 1. Observation
• 1. Feature
• ...and 4 more

Python Environment (PYENV)

Count: 15 concepts (5.0%)

Concepts:

• 1. Jupyter Notebooks
• 1. Python Installation
• 1. Package Management
• 1. Pip
• 1. Conda Environment
• 1. Virtual Environment
• 1. IDE Setup
• 1. VS Code
• 1. Notebook Cells
• 1. Code Cell
• 1. Markdown Cell
• 1. Cell Execution
• 1. Kernel
• 1. Import Statement
• 1. Python Libraries

NumPy Computing (NUMPY)

Count: 15 concepts (5.0%)

Concepts:

• 1. NumPy Library
• 1. NumPy Array
• 1. Array Creation
• 1. Array Shape
• 1. Array Indexing
• 1. Array Slicing
• 1. Broadcasting
• 1. Vectorized Operations
• 1. Element-wise Operations
• 1. Matrix Operations
• 1. Dot Product
• 1. Matrix Multiplication
• 1. Transpose
• 1. Linear Algebra
• 1. Computational Efficiency

Best Practices (BEST)

Count: 11 concepts (3.7%)

Concepts:

• 1. Documentation
• 1. Explainable AI
• 1. Model Interpretability
• 1. Feature Importance Analysis
• 1. SHAP Values
• 1. Model Documentation
• 1. Reproducibility
• 1. Random Seed
• 1. Version Control
• 1. Git
• 1. Data Ethics

Projects (PROJ)

Count: 5 concepts (1.7%)

Concepts:

• 1. Capstone Project
• 1. End-to-End Pipeline
• 1. Model Deployment
• 1. Results Communication
• 1. Data-Driven Decisions

Recommendations

• Excellent balance: Categories are evenly distributed (spread: 8.3%)
• MISC category minimal: Good categorization specificity

Educational Use Recommendations

• Use taxonomy categories for color-coding in graph visualizations
• Design curriculum modules based on taxonomy groupings
• Create filtered views for focused learning paths
• Use categories for assessment organization
• Enable navigation by topic area in interactive tools

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Report generated by taxonomy-distribution.py