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DATA SCIENTIST - Coggle Diagram

- - - - Arrays (creation, indexing, slicing)
      - Broadcasting
      - Vectorized operations
      - Linear algebra (dot, inv, eig, svd)
      - Random module (rand, randn, seed)
  - - - Series and DataFrame creation
      - Indexing, filtering, slicing
      - GroupBy, pivot, crosstab
      - Merging, joining, concatenation
      - Missing values (fillna, dropna)
      - DateTime operations
  - - - Data types (int, float, string, bool)
      - Lists, tuples, sets, dicts
      - Loops, conditionals, functions
      - List comprehensions, generators
      - OOP (class, object, inheritance, polymorphism)
      - Modules, packages
      - Error handling (try, except, finally)
      - File handling (read, write, append)
  - - - Line, bar, scatter, histogram, boxplot, heatmap
      - Styling (colors, labels, titles, legends)
      - Subplots, figure size
- - - - Functions, limits, continuity
      - Differentiation rules (power, product, quotient, chain)
      - Partial derivatives
      - Gradient, Jacobian, Hessian
      - Taylor expansion
      - Integration (definite, indefinite, substitution, by parts)
      - Double and triple integrals
  - - - Random variables (discrete, continuous)
      - Probability axioms, Bayes theorem
      - Conditional probability
      - Expectation, variance, covariance
      - Joint, marginal, conditional distributions
      - Law of large numbers, Central limit theorem
  - - - Scalars, vectors, matrices, tensors
      - Vector addition, scalar multiplication
      - Dot product, cross product
      - Matrix addition, multiplication, transpose
      - Determinant, inverse, rank
      - Eigenvalues, eigenvectors
      - Singular Value Decomposition (SVD)
  - - - Descriptive statistics (mean, median, mode, std, variance, skewness, kurtosis)
      - Sampling methods (random, stratified, cluster)
      - Hypothesis testing (null, alternate, p-value, significance)
      - Z-test, t-test, chi-square test, ANOVA
      - Confidence intervals
      - Correlation vs causation
      - Regression assumptions
- - - - Linear regression (OLS, assumptions, residual analysis)
        
        Ridge, Lasso, ElasticNet
        
        Polynomial regression
        
        Decision Tree regression
        
        Random Forest regression
        
        Gradient Boosting (XGBoost, LightGBM, CatBoost)
    - - Logistic regression (sigmoid, odds ratio, logit)
        
        Decision Tree classifier
        
        Random Forest classifier
        
        Naïve Bayes (Gaussian, Multinomial, Bernoulli)
        
        k-NN
        
        SVM (linear, kernel tricks)
        
        Ensemble methods (bagging, boosting, stacking)
        
        ROC, AUC, Precision, Recall, F1-score
  - - - Clustering (k-means, hierarchical, DBSCAN)
      - Dimensionality reduction (PCA, t-SNE, UMAP)
      - Association rules (Apriori, FP-growth)
  - - - Train-test split, cross-validation
      - Bias-variance tradeoff
      - Confusion matrix, classification report
      - Regression metrics (MSE, RMSE, MAE, R²)
      - Feature importance