--- type: CKG Bundle title: Data Science Course tags: [Business & Data] timestamp: 2026-06-18T00:00:00Z ckg: id: data-science-course nodes: 300 license: CC BY 4.0 --- # Data Science Course — Compressed Knowledge Graph ```csv ConceptID,ConceptLabel,TaxonomyID 1,Data Science,FOUND 2,Python Programming,FOUND 3,Jupyter Notebooks,PYENV 4,Data,FOUND 5,Variables,FOUND 6,Data Types,FOUND 7,Numerical Data,FOUND 8,Categorical Data,FOUND 9,Ordinal Data,FOUND 10,Nominal Data,FOUND 11,Measurement Scales,FOUND 12,Independent Variable,FOUND 13,Dependent Variable,FOUND 14,Dataset,FOUND 15,Observation,FOUND 16,Feature,FOUND 17,Target Variable,FOUND 18,Data Science Workflow,FOUND 19,Problem Definition,FOUND 20,Data Collection,FOUND 21,Python Installation,PYENV 22,Package Management,PYENV 23,Pip,PYENV 24,Conda Environment,PYENV 25,Virtual Environment,PYENV 26,IDE Setup,PYENV 27,VS Code,PYENV 28,Notebook Cells,PYENV 29,Code Cell,PYENV 30,Markdown Cell,PYENV 31,Cell Execution,PYENV 32,Kernel,PYENV 33,Import Statement,PYENV 34,Python Libraries,PYENV 35,Documentation,BEST 36,Lists,DSTRC 37,Dictionaries,DSTRC 38,Tuples,DSTRC 39,Arrays,DSTRC 40,Pandas Library,DSTRC 41,DataFrame,DSTRC 42,Series,DSTRC 43,Index,DSTRC 44,Column,DSTRC 45,Row,DSTRC 46,Data Loading,DSTRC 47,CSV Files,DSTRC 48,Read CSV,DSTRC 49,Data Inspection,DSTRC 50,Head Method,DSTRC 51,Tail Method,DSTRC 52,Shape Attribute,DSTRC 53,Info Method,DSTRC 54,Describe Method,DSTRC 55,Data Selection,DSTRC 56,Missing Values,CLEAN 57,NaN,CLEAN 58,Null Detection,CLEAN 59,Dropna Method,CLEAN 60,Fillna Method,CLEAN 61,Imputation,CLEAN 62,Data Type Conversion,CLEAN 63,Duplicate Detection,CLEAN 64,Duplicate Removal,CLEAN 65,Outliers,CLEAN 66,Outlier Detection,CLEAN 67,Data Validation,CLEAN 68,String Cleaning,CLEAN 69,Column Renaming,CLEAN 70,Data Filtering,CLEAN 71,Boolean Indexing,CLEAN 72,Query Method,CLEAN 73,Data Transformation,CLEAN 74,Feature Scaling,CLEAN 75,Normalization,CLEAN 76,Data Visualization,VIZ 77,Matplotlib Library,VIZ 78,Figure,VIZ 79,Axes,VIZ 80,Plot Function,VIZ 81,Line Plot,VIZ 82,Scatter Plot,VIZ 83,Bar Chart,VIZ 84,Histogram,VIZ 85,Box Plot,VIZ 86,Pie Chart,VIZ 87,Subplot,VIZ 88,Figure Size,VIZ 89,Title,VIZ 90,Axis Labels,VIZ 91,Legend,VIZ 92,Color,VIZ 93,Markers,VIZ 94,Line Styles,VIZ 95,Grid,VIZ 96,Annotations,VIZ 97,Save Figure,VIZ 98,Plot Customization,VIZ 99,Seaborn Library,VIZ 100,Statistical Plots,VIZ 101,Descriptive Statistics,STATS 102,Mean,STATS 103,Median,STATS 104,Mode,STATS 105,Range,STATS 106,Variance,STATS 107,Standard Deviation,STATS 108,Quartiles,STATS 109,Percentiles,STATS 110,Interquartile Range,STATS 111,Skewness,STATS 112,Kurtosis,STATS 113,Distribution,STATS 114,Normal Distribution,STATS 115,Probability,STATS 116,Random Variables,STATS 117,Expected Value,STATS 118,Sample,STATS 119,Population,STATS 120,Sampling,STATS 121,Central Limit Theorem,STATS 122,Confidence Interval,STATS 123,Hypothesis Testing,STATS 124,P-Value,STATS 125,Statistical Significance,STATS 126,Correlation,STATS 127,Covariance,STATS 128,Pearson Correlation,STATS 129,Spearman Correlation,STATS 130,Correlation Matrix,STATS 131,Regression Analysis,REGR 132,Linear Regression,REGR 133,Simple Linear Regression,REGR 134,Regression Line,REGR 135,Slope,REGR 136,Intercept,REGR 137,Least Squares Method,REGR 138,Residuals,REGR 139,Sum of Squared Errors,REGR 140,Ordinary Least Squares,REGR 141,Regression Coefficients,REGR 142,Coefficient Interpretation,REGR 143,Prediction,REGR 144,Fitted Values,REGR 145,Regression Equation,REGR 146,Line of Best Fit,REGR 147,Assumptions of Regression,REGR 148,Linearity Assumption,REGR 149,Homoscedasticity,REGR 150,Independence Assumption,REGR 151,Normality of Residuals,REGR 152,Scikit-learn Library,REGR 153,LinearRegression Class,REGR 154,Fit Method,REGR 155,Predict Method,REGR 156,Model Performance,EVAL 157,Training Data,EVAL 158,Testing Data,EVAL 159,Train Test Split,EVAL 160,Validation Data,EVAL 161,R-Squared,EVAL 162,Adjusted R-Squared,EVAL 163,Mean Squared Error,EVAL 164,Root Mean Squared Error,EVAL 165,Mean Absolute Error,EVAL 166,Residual Analysis,EVAL 167,Residual Plot,EVAL 168,Overfitting,EVAL 169,Underfitting,EVAL 170,Bias,EVAL 171,Variance,EVAL 172,Bias-Variance Tradeoff,EVAL 173,Model Complexity,EVAL 174,Cross-Validation,EVAL 175,K-Fold Cross-Validation,EVAL 176,Leave One Out CV,EVAL 177,Holdout Method,EVAL 178,Model Selection,EVAL 179,Hyperparameters,EVAL 180,Model Comparison,EVAL 181,Multiple Linear Regression,ADVR 182,Multiple Predictors,ADVR 183,Multicollinearity,ADVR 184,Variance Inflation Factor,ADVR 185,Feature Selection,ADVR 186,Forward Selection,ADVR 187,Backward Elimination,ADVR 188,Stepwise Selection,ADVR 189,Categorical Variables,ADVR 190,Dummy Variables,ADVR 191,One-Hot Encoding,ADVR 192,Interaction Terms,ADVR 193,Polynomial Features,ADVR 194,Feature Engineering,ADVR 195,Feature Importance,ADVR 196,NumPy Library,NUMPY 197,NumPy Array,NUMPY 198,Array Creation,NUMPY 199,Array Shape,NUMPY 200,Array Indexing,NUMPY 201,Array Slicing,NUMPY 202,Broadcasting,NUMPY 203,Vectorized Operations,NUMPY 204,Element-wise Operations,NUMPY 205,Matrix Operations,NUMPY 206,Dot Product,NUMPY 207,Matrix Multiplication,NUMPY 208,Transpose,NUMPY 209,Linear Algebra,NUMPY 210,Computational Efficiency,NUMPY 211,Non-linear Regression,ADVR 212,Polynomial Regression,ADVR 213,Degree of Polynomial,ADVR 214,Curve Fitting,ADVR 215,Transformation,ADVR 216,Log Transformation,ADVR 217,Feature Transformation,ADVR 218,Model Flexibility,ADVR 219,Regularization,ADVR 220,Ridge Regression,ADVR 221,Lasso Regression,ADVR 222,Elastic Net,ADVR 223,Regularization Parameter,ADVR 224,Lambda Parameter,ADVR 225,Shrinkage,ADVR 226,Machine Learning,ML 227,Supervised Learning,ML 228,Unsupervised Learning,ML 229,Classification,ML 230,Clustering,ML 231,Training Process,ML 232,Learning Algorithm,ML 233,Model Training,ML 234,Generalization,ML 235,Training Error,ML 236,Test Error,ML 237,Prediction Error,ML 238,Loss Function,ML 239,Cost Function,ML 240,Optimization,ML 241,Gradient Descent,ML 242,Learning Rate,ML 243,Convergence,ML 244,Local Minimum,ML 245,Global Minimum,ML 246,Neural Networks,NN 247,Artificial Neuron,NN 248,Perceptron,NN 249,Activation Function,NN 250,Sigmoid Function,NN 251,ReLU Function,NN 252,Input Layer,NN 253,Hidden Layer,NN 254,Output Layer,NN 255,Weights,NN 256,Biases,NN 257,Forward Propagation,NN 258,Backpropagation,NN 259,Deep Learning,NN 260,Network Architecture,NN 261,Epochs,NN 262,Batch Size,NN 263,Mini-batch,NN 264,Stochastic Gradient,NN 265,Vanishing Gradient,NN 266,PyTorch Library,TORCH 267,Tensors,TORCH 268,Tensor Operations,TORCH 269,Autograd,TORCH 270,Automatic Differentiation,TORCH 271,Computational Graph,TORCH 272,Neural Network Module,TORCH 273,Sequential Model,TORCH 274,Linear Layer,TORCH 275,Loss Functions PyTorch,TORCH 276,Optimizer,TORCH 277,SGD Optimizer,TORCH 278,Adam Optimizer,TORCH 279,Training Loop,TORCH 280,Model Evaluation PyTorch,TORCH 281,GPU Computing,TORCH 282,CUDA,TORCH 283,Model Saving,TORCH 284,Model Loading,TORCH 285,Transfer Learning,TORCH 286,Explainable AI,BEST 287,Model Interpretability,BEST 288,Feature Importance Analysis,BEST 289,SHAP Values,BEST 290,Model Documentation,BEST 291,Reproducibility,BEST 292,Random Seed,BEST 293,Version Control,BEST 294,Git,BEST 295,Data Ethics,BEST 296,Capstone Project,PROJ 297,End-to-End Pipeline,PROJ 298,Model Deployment,PROJ 299,Results Communication,PROJ 300,Data-Driven Decisions,PROJ ``` ## Edges (prerequisite -> concept) 2 -> 3 1 -> 4 2 -> 5 4 -> 6 5 -> 6 6 -> 7 6 -> 8 8 -> 9 8 -> 10 7 -> 11 8 -> 11 9 -> 11 10 -> 11 5 -> 12 4 -> 12 5 -> 13 4 -> 13 4 -> 14 14 -> 15 14 -> 16 12 -> 16 13 -> 17 14 -> 17 1 -> 18 4 -> 18 18 -> 19 18 -> 20 19 -> 20 2 -> 21 21 -> 22 22 -> 23 22 -> 24 22 -> 25 21 -> 26 26 -> 27 3 -> 28 28 -> 29 28 -> 30 29 -> 31 3 -> 32 31 -> 32 2 -> 33 33 -> 34 22 -> 34 2 -> 35 5 -> 36 5 -> 37 5 -> 38 36 -> 39 34 -> 40 39 -> 40 40 -> 41 40 -> 42 41 -> 43 41 -> 44 41 -> 45 40 -> 46 14 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300 1 -> 300 *Free CKG · Graphify.md · `pip install ckg-mcp` for all · own the knowledge layer, rent the model*