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