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Welcome to the ds-resources wiki! This wiki provides a reference guide to the subjects and code samples provided in the other parts of this repository.

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Data Analysis Roadmap

Analytical Workflow

1. Problem Specification
2. Data Acquisition
3. Data Preparation
   • Data Cleaning
   • Plotting Methods
   • Exploratory Data Analysis (EDA)
   • Feature Engineering and Extraction
   • Feature Selection
4. Modeling
   • Prediction
     • Regression Models
       • Simple Linear Regression (SLR)
       • Multiple Linear Regression (MLR)
       • Polynomial Regression
       • LASSO Regression
       • Ridge Regression
       • ElasticNet Regression
     • DTR
     • Neural Networks
   • Classification
     • DTC
     • KNN
     • SVM
     • Neural Networks
     • Ensemble Methods
       • Bagging
       • Boosting
5. Model Evaluation
   • Prediction
     • R^2
     • Adjusted R^2
     • Mean Square Error (MSE)
     • Root Mean Square Error (RMSE)
     • Median Absolute Error (MedAE)
     • Mean Square Log Error (MSLE)
   • Classification
     • Accuracy
     • Precision/Recall/F1
     • ROC/AUC
6. Interpretation and Communication of Results
7. Model Deployment

Statistical Theory

• Distributions
• Descriptive Statistics
• Inferential Statistics

Tools and Resources

• Python
  • General Python reference and documentation
  • Libraries
    • Data Manipulation and Analysis
      • NumPy
      • Pandas
      • scikit-learn
      • StatsModels
    • Plotting and Graphics
      • matplotlib
      • Plotly
      • Seaborn
• SQL