| ConceptID,ConceptLabel,Dependencies,TaxonomyID |
| 1,Data Science,,FOUND |
| 2,Python Programming,,FOUND |
| 3,Jupyter Notebooks,2,PYENV |
| 4,Data,1,FOUND |
| 5,Variables,2,FOUND |
| 6,Data Types,4|5,FOUND |
| 7,Numerical Data,6,FOUND |
| 8,Categorical Data,6,FOUND |
| 9,Ordinal Data,8,FOUND |
| 10,Nominal Data,8,FOUND |
| 11,Measurement Scales,7|8|9|10,FOUND |
| 12,Independent Variable,5|4,FOUND |
| 13,Dependent Variable,5|4,FOUND |
| 14,Dataset,4,FOUND |
| 15,Observation,14,FOUND |
| 16,Feature,14|12,FOUND |
| 17,Target Variable,13|14,FOUND |
| 18,Data Science Workflow,1|4,FOUND |
| 19,Problem Definition,18,FOUND |
| 20,Data Collection,18|19,FOUND |
| 21,Python Installation,2,PYENV |
| 22,Package Management,21,PYENV |
| 23,Pip,22,PYENV |
| 24,Conda Environment,22,PYENV |
| 25,Virtual Environment,22,PYENV |
| 26,IDE Setup,21,PYENV |
| 27,VS Code,26,PYENV |
| 28,Notebook Cells,3,PYENV |
| 29,Code Cell,28,PYENV |
| 30,Markdown Cell,28,PYENV |
| 31,Cell Execution,29,PYENV |
| 32,Kernel,3|31,PYENV |
| 33,Import Statement,2,PYENV |
| 34,Python Libraries,33|22,PYENV |
| 35,Documentation,2,BEST |
| 36,Lists,5,DSTRC |
| 37,Dictionaries,5,DSTRC |
| 38,Tuples,5,DSTRC |
| 39,Arrays,36,DSTRC |
| 40,Pandas Library,34|39,DSTRC |
| 41,DataFrame,40,DSTRC |
| 42,Series,40,DSTRC |
| 43,Index,41,DSTRC |
| 44,Column,41,DSTRC |
| 45,Row,41,DSTRC |
| 46,Data Loading,40|14,DSTRC |
| 47,CSV Files,46,DSTRC |
| 48,Read CSV,47|40,DSTRC |
| 49,Data Inspection,41,DSTRC |
| 50,Head Method,49,DSTRC |
| 51,Tail Method,49,DSTRC |
| 52,Shape Attribute,49,DSTRC |
| 53,Info Method,49,DSTRC |
| 54,Describe Method,49,DSTRC |
| 55,Data Selection,41|43|44,DSTRC |
| 56,Missing Values,41|4,CLEAN |
| 57,NaN,56,CLEAN |
| 58,Null Detection,57,CLEAN |
| 59,Dropna Method,58,CLEAN |
| 60,Fillna Method,58,CLEAN |
| 61,Imputation,60|101,CLEAN |
| 62,Data Type Conversion,6|41,CLEAN |
| 63,Duplicate Detection,41,CLEAN |
| 64,Duplicate Removal,63,CLEAN |
| 65,Outliers,41|101,CLEAN |
| 66,Outlier Detection,65|84,CLEAN |
| 67,Data Validation,41|56,CLEAN |
| 68,String Cleaning,41,CLEAN |
| 69,Column Renaming,41,CLEAN |
| 70,Data Filtering,55,CLEAN |
| 71,Boolean Indexing,70|36,CLEAN |
| 72,Query Method,70,CLEAN |
| 73,Data Transformation,41,CLEAN |
| 74,Feature Scaling,73|101,CLEAN |
| 75,Normalization,74,CLEAN |
| 76,Data Visualization,4|1,VIZ |
| 77,Matplotlib Library,34|76,VIZ |
| 78,Figure,77,VIZ |
| 79,Axes,78,VIZ |
| 80,Plot Function,79,VIZ |
| 81,Line Plot,80,VIZ |
| 82,Scatter Plot,80,VIZ |
| 83,Bar Chart,80,VIZ |
| 84,Histogram,80|113,VIZ |
| 85,Box Plot,80|108,VIZ |
| 86,Pie Chart,80,VIZ |
| 87,Subplot,78,VIZ |
| 88,Figure Size,78,VIZ |
| 89,Title,79,VIZ |
| 90,Axis Labels,79,VIZ |
| 91,Legend,79,VIZ |
| 92,Color,79,VIZ |
| 93,Markers,82,VIZ |
| 94,Line Styles,81,VIZ |
| 95,Grid,79,VIZ |
| 96,Annotations,79,VIZ |
| 97,Save Figure,78,VIZ |
| 98,Plot Customization,89|90|91|92,VIZ |
| 99,Seaborn Library,77|100,VIZ |
| 100,Statistical Plots,76|101,VIZ |
| 101,Descriptive Statistics,4|11,STATS |
| 102,Mean,101,STATS |
| 103,Median,101,STATS |
| 104,Mode,101,STATS |
| 105,Range,101,STATS |
| 106,Variance,102,STATS |
| 107,Standard Deviation,106,STATS |
| 108,Quartiles,103,STATS |
| 109,Percentiles,108,STATS |
| 110,Interquartile Range,108,STATS |
| 111,Skewness,102|103,STATS |
| 112,Kurtosis,111,STATS |
| 113,Distribution,101,STATS |
| 114,Normal Distribution,113|102|107,STATS |
| 115,Probability,113,STATS |
| 116,Random Variables,115|5,STATS |
| 117,Expected Value,116|102,STATS |
| 118,Sample,14|115,STATS |
| 119,Population,118,STATS |
| 120,Sampling,118|119,STATS |
| 121,Central Limit Theorem,114|120,STATS |
| 122,Confidence Interval,121|107,STATS |
| 123,Hypothesis Testing,122|115,STATS |
| 124,P-Value,123,STATS |
| 125,Statistical Significance,124,STATS |
| 126,Correlation,12|13|101,STATS |
| 127,Covariance,126|106,STATS |
| 128,Pearson Correlation,126,STATS |
| 129,Spearman Correlation,126|9,STATS |
| 130,Correlation Matrix,128|41,STATS |
| 131,Regression Analysis,126|12|13,REGR |
| 132,Linear Regression,131,REGR |
| 133,Simple Linear Regression,132,REGR |
| 134,Regression Line,133|81,REGR |
| 135,Slope,134,REGR |
| 136,Intercept,134,REGR |
| 137,Least Squares Method,138|139,REGR |
| 138,Residuals,133|143,REGR |
| 139,Sum of Squared Errors,138,REGR |
| 140,Ordinary Least Squares,137,REGR |
| 141,Regression Coefficients,135|136,REGR |
| 142,Coefficient Interpretation,141,REGR |
| 143,Prediction,133,REGR |
| 144,Fitted Values,143,REGR |
| 145,Regression Equation,141,REGR |
| 146,Line of Best Fit,137|134,REGR |
| 147,Assumptions of Regression,132,REGR |
| 148,Linearity Assumption,147,REGR |
| 149,Homoscedasticity,147|138,REGR |
| 150,Independence Assumption,147,REGR |
| 151,Normality of Residuals,147|138|114,REGR |
| 152,Scikit-learn Library,34|132,REGR |
| 153,LinearRegression Class,152,REGR |
| 154,Fit Method,153,REGR |
| 155,Predict Method,153|143,REGR |
| 156,Model Performance,143|132,EVAL |
| 157,Training Data,14|156,EVAL |
| 158,Testing Data,14|156,EVAL |
| 159,Train Test Split,157|158|152,EVAL |
| 160,Validation Data,157|158,EVAL |
| 161,R-Squared,156|139,EVAL |
| 162,Adjusted R-Squared,161|181,EVAL |
| 163,Mean Squared Error,156|139,EVAL |
| 164,Root Mean Squared Error,163,EVAL |
| 165,Mean Absolute Error,156|138,EVAL |
| 166,Residual Analysis,138|167,EVAL |
| 167,Residual Plot,138|82,EVAL |
| 168,Overfitting,156|173,EVAL |
| 169,Underfitting,156|173,EVAL |
| 170,Bias,168|169,EVAL |
| 171,Variance,168|106,EVAL |
| 172,Bias-Variance Tradeoff,170|171,EVAL |
| 173,Model Complexity,132|156,EVAL |
| 174,Cross-Validation,159|168,EVAL |
| 175,K-Fold Cross-Validation,174,EVAL |
| 176,Leave One Out CV,174,EVAL |
| 177,Holdout Method,159,EVAL |
| 178,Model Selection,174|172,EVAL |
| 179,Hyperparameters,178,EVAL |
| 180,Model Comparison,178|161,EVAL |
| 181,Multiple Linear Regression,132|182,ADVR |
| 182,Multiple Predictors,12|16,ADVR |
| 183,Multicollinearity,181|126,ADVR |
| 184,Variance Inflation Factor,183,ADVR |
| 185,Feature Selection,181|16,ADVR |
| 186,Forward Selection,185,ADVR |
| 187,Backward Elimination,185,ADVR |
| 188,Stepwise Selection,186|187,ADVR |
| 189,Categorical Variables,8|181,ADVR |
| 190,Dummy Variables,189,ADVR |
| 191,One-Hot Encoding,190,ADVR |
| 192,Interaction Terms,181|194,ADVR |
| 193,Polynomial Features,181,ADVR |
| 194,Feature Engineering,16|73,ADVR |
| 195,Feature Importance,185|141,ADVR |
| 196,NumPy Library,34|39,NUMPY |
| 197,NumPy Array,196,NUMPY |
| 198,Array Creation,197,NUMPY |
| 199,Array Shape,197,NUMPY |
| 200,Array Indexing,197,NUMPY |
| 201,Array Slicing,200,NUMPY |
| 202,Broadcasting,197|204,NUMPY |
| 203,Vectorized Operations,197,NUMPY |
| 204,Element-wise Operations,203,NUMPY |
| 205,Matrix Operations,197,NUMPY |
| 206,Dot Product,205,NUMPY |
| 207,Matrix Multiplication,205,NUMPY |
| 208,Transpose,205,NUMPY |
| 209,Linear Algebra,205,NUMPY |
| 210,Computational Efficiency,203|196,NUMPY |
| 211,Non-linear Regression,132,ADVR |
| 212,Polynomial Regression,211|193,ADVR |
| 213,Degree of Polynomial,212,ADVR |
| 214,Curve Fitting,211,ADVR |
| 215,Transformation,73|211,ADVR |
| 216,Log Transformation,215,ADVR |
| 217,Feature Transformation,215|194,ADVR |
| 218,Model Flexibility,173|211,ADVR |
| 219,Regularization,168|181,ADVR |
| 220,Ridge Regression,219,ADVR |
| 221,Lasso Regression,219,ADVR |
| 222,Elastic Net,220|221,ADVR |
| 223,Regularization Parameter,219,ADVR |
| 224,Lambda Parameter,223,ADVR |
| 225,Shrinkage,219|141,ADVR |
| 226,Machine Learning,1|132,ML |
| 227,Supervised Learning,226|17,ML |
| 228,Unsupervised Learning,226,ML |
| 229,Classification,227,ML |
| 230,Clustering,228,ML |
| 231,Training Process,227|233,ML |
| 232,Learning Algorithm,226,ML |
| 233,Model Training,232|157,ML |
| 234,Generalization,233|158,ML |
| 235,Training Error,233|163,ML |
| 236,Test Error,234|163,ML |
| 237,Prediction Error,236,ML |
| 238,Loss Function,239|163,ML |
| 239,Cost Function,137,ML |
| 240,Optimization,239,ML |
| 241,Gradient Descent,240,ML |
| 242,Learning Rate,241,ML |
| 243,Convergence,241,ML |
| 244,Local Minimum,240,ML |
| 245,Global Minimum,244,ML |
| 246,Neural Networks,226,NN |
| 247,Artificial Neuron,246,NN |
| 248,Perceptron,247,NN |
| 249,Activation Function,247,NN |
| 250,Sigmoid Function,249,NN |
| 251,ReLU Function,249,NN |
| 252,Input Layer,246|16,NN |
| 253,Hidden Layer,246,NN |
| 254,Output Layer,246|17,NN |
| 255,Weights,247|141,NN |
| 256,Biases,247|136,NN |
| 257,Forward Propagation,252|253|254,NN |
| 258,Backpropagation,257|241,NN |
| 259,Deep Learning,246|253,NN |
| 260,Network Architecture,246,NN |
| 261,Epochs,231|279,NN |
| 262,Batch Size,231,NN |
| 263,Mini-batch,262,NN |
| 264,Stochastic Gradient,241|263,NN |
| 265,Vanishing Gradient,258|250,NN |
| 266,PyTorch Library,34|246,TORCH |
| 267,Tensors,266|197,TORCH |
| 268,Tensor Operations,267,TORCH |
| 269,Autograd,266,TORCH |
| 270,Automatic Differentiation,241|269,TORCH |
| 271,Computational Graph,269,TORCH |
| 272,Neural Network Module,266,TORCH |
| 273,Sequential Model,272,TORCH |
| 274,Linear Layer,273,TORCH |
| 275,Loss Functions PyTorch,266|238,TORCH |
| 276,Optimizer,266|240,TORCH |
| 277,SGD Optimizer,276|241,TORCH |
| 278,Adam Optimizer,276,TORCH |
| 279,Training Loop,273|275|276,TORCH |
| 280,Model Evaluation PyTorch,279|156,TORCH |
| 281,GPU Computing,266|210,TORCH |
| 282,CUDA,281,TORCH |
| 283,Model Saving,279,TORCH |
| 284,Model Loading,283,TORCH |
| 285,Transfer Learning,259|284,TORCH |
| 286,Explainable AI,226,BEST |
| 287,Model Interpretability,142|286,BEST |
| 288,Feature Importance Analysis,195|287,BEST |
| 289,SHAP Values,288,BEST |
| 290,Model Documentation,35|287,BEST |
| 291,Reproducibility,290,BEST |
| 292,Random Seed,116|291,BEST |
| 293,Version Control,291,BEST |
| 294,Git,293,BEST |
| 295,Data Ethics,1|286,BEST |
| 296,Capstone Project,18,PROJ |
| 297,End-to-End Pipeline,18|296,PROJ |
| 298,Model Deployment,279|297,PROJ |
| 299,Results Communication,76|290,PROJ |
| 300,Data-Driven Decisions,299|1,PROJ |
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