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Advanced Interactive Learning Path
Master the basics: Population vs Sample, Types of Data (Nominal, Ordinal), and the data science workflow.
Mean, Median, Mode. Learn when to use which metric depending on outliers and skewness.
Variance, Standard Deviation, IQR. Understanding the "spread" and variability of your data.
Independent events, Conditional probability, Rules of addition/multiplication, and Bayes' Theorem.
The landscape of random variables: Understanding Discrete (PMF) vs Continuous (PDF) functions.
Modeling success/failure scenarios (Bernoulli trials) and counting rare events occurring in time intervals.
The Bell Curve. Standardization using Z-Scores and applying the Empirical Rule (68-95-99.7).
Central Limit Theorem: The magical reason why averages of samples tend to look Normal, regardless of population.
Making decisions with data. P-values, Null Hypothesis, T-tests, and understanding Type I/II Errors.
Pearson vs Spearman relationships. Measuring the strength and direction of linear associations.
Understanding joint variability. How two variables change together, leading to the Covariance Matrix.
Simple and Multiple Linear Regression models. Interpreting coefficients, R-squared, and checking assumptions.
Analysis of Variance. Testing statistically significant differences between means of three or more groups.
