Experimental Physics

1. Data Analysis
   1. Statistical Methods
      1. Descriptive Statistics
         1. Measures of central tendency
            1. Mean
            2. Median
            3. Mode
         2. Measures of dispersion
            1. Variance
            2. Standard deviation
            3. Range
         3. Distribution shape
            1. Skewness
            2. Kurtosis
      2. Inferential Statistics
         1. Hypothesis testing
            1. Null and alternative hypotheses
            2. Type I and Type II errors
         2. Confidence intervals
         3. p-values and statistical significance
         4. T-tests and Z-tests
         5. ANOVA (Analysis of Variance)
         6. Chi-square tests
         7. Correlation and regression analysis
            1. Pearson correlation
            2. Spearman rank correlation
            3. Linear regression models
            4. Multiple regression analysis
   2. Error Analysis
      1. Types of Errors
         1. Systematic errors
         2. Random errors
         3. Measurement uncertainty
      2. Propagation of Errors
         1. Error propagation formulas
         2. Combining uncertainties
      3. Error Analysis Techniques
         1. Residual analysis
         2. Goodness-of-fit checks
         3. Confidence intervals for errors
   3. Signal Processing
      1. Signal Types
         1. Analog signals
         2. Digital signals
      2. Filtering Techniques
         1. Low-pass filters
         2. High-pass filters
         3. Band-pass filters
         4. Fourier transform and spectral analysis
      3. Noise Reduction
         1. Signal-to-noise ratio improvement
         2. Statistical bootstrapping
         3. Smoothing methods
      4. Time Series Analysis
         1. Trend analysis
         2. Seasonal decomposition
         3. Autoregressive models
   4. Data Modeling
      1. Model Types
         1. Predictive models
            1. Linear models
            2. Non-linear models
            3. Time series forecasting
         2. Descriptive models
            1. Clustering techniques
            2. Dimensionality reduction
         3. Simulation models
            1. Monte Carlo simulations
            2. Agent-based modeling
      2. Model Validation
         1. Cross-validation techniques
         2. Training and test data splits
         3. Overfitting prevention
      3. Model Evaluation
         1. Performance metrics
            1. Accuracy
            2. Precision and recall
            3. F1 Score
            4. ROC curves and AUC
         2. Diagnostic checks
            1. Residual plots
            2. Assumption testing (normality, homoscedasticity)
   5. Data Visualization
      1. Types of Visualizations
         1. Histograms and bar charts
         2. Scatter plots and line graphs
         3. Box plots
         4. Heatmaps
      2. Tools and Software
         1. Python libraries (Matplotlib, Seaborn)
         2. Software packages (R, MATLAB)
         3. Interactive dashboards (Tableau, PowerBI)
      3. Best Practices
         1. Designing for clarity and impact
         2. Using color effectively
         3. Audience consideration
   6. Advanced Topics
      1. Machine Learning Integration
         1. Supervised learning methods
         2. Unsupervised learning methods
         3. Reinforcement learning concepts
      2. Big Data Techniques
         1. Data mining strategies
         2. Data warehousing
         3. Apache Hadoop and Spark platforms
      3. Data Ethics and Privacy
         1. Ethical considerations in data usage
         2. Data anonymization techniques
         3. Compliance with data protection regulations (GDPR, CCPA)