📊 Statistics Roadmap (Beginner → Advanced)

1. Foundations of Statistics
   • Population vs Sample
   • Types of Data (qualitative vs quantitative, discrete vs continuous)
   • Scales of Measurement (nominal, ordinal, interval, ratio)
2. Descriptive Statistics
   • Measures of Central Tendency: mean, median, mode
   • Measures of Spread: variance, standard deviation, IQR
   • Data Visualization: histograms, boxplots, scatterplots
3. Probability Basics
   • Random experiments, events, sample space
   • Probability rules (addition, multiplication)
   • Conditional probability & Bayes’ theorem
   • Random variables (discrete & continuous)
4. Probability Distributions
   • Discrete: Binomial, Poisson
   • Continuous: Normal, Exponential, Uniform
   • Central Limit Theorem (CLT)
5. Inferential Statistics
   • Sampling methods & sampling distribution
   • Confidence intervals
   • Hypothesis testing (null vs alternative, p-value, significance)
   • Common tests: z-test, t-test, chi-square, ANOVA
6. Correlation & Regression
   • Correlation vs causation
   • Simple linear regression
   • Multiple regression
   • Goodness of fit (R², adjusted R²)
7. Advanced Topics (for Data Science)
   • Logistic regression (classification)
   • Non-parametric tests
   • Resampling methods (bootstrapping, permutation tests)
   • Introduction to Bayesian statistics

I suggest we go step by step, with small explanations, examples, and exercises.