Statistics (SOON)
Statistics is everywhere, changing our understanding of the world. From predicting rain, to diagnose danger diseases, statistics is more than numbers. You can consider statistics as the art of turning numbers into insights.
Course Outline
The foundations of Statistics
Lecture 1. What is Statistics? Types (Descriptive vs Inferential) (SOON)
Lecture 2. Types of Data (quantitative, qualitative, scales of measurement) (SOON)
Lecture 3. Data collection - population, samples, sampling methods (SOON)
Lecture 4. Summarising data (tables, graphs, frequency) (SOON)
Lecture 5. Measures of central tendency (mean, median, mode) (SOON)
Variablility and probability
Lecture 6. Measures of spread (range, variance, standard deviation, IQR) (SOON)
Lecture 7. Visualising probabilities - box plots, histograms, and distribution shapes (SOON)
Lecture 8. Conditional probability theory (SOON)
Lecture 9. Conditional probability, Bayes’ theorem (SOON)
Lecture 10. Discreate vs continuous variables and distributions (SOON)
Distributions deep dive
Lecture 11. The Normal distribution and z-scores (SOON)
Lecture 12. Binomial distribution (SOON)
Lecture 13. Poisson distribution (SOON)
Lecture 14. Centeral limit theorem (CLT) (SOON)
Lecture 15. Sampling distributions and standard error (SOON)
Inferential Statistics
Lecture 16. Point estimates & Confidence intervals (SOON)
Lecture 17. Hypothesis testing (steps, errors, significance level) (SOON)
Lecture 18. One-sample t-test (SOON)
Lecture 19. Two-sample t-test and paired t-test (SOON)
Lecture 20. p-values, effect size, and statistical power (SOON)
Categorical data and correlation
Lecture 21. Chi-Square test for independence (SOON)
Lecture 22. Fisher’s exact test (SOON)
Lecture 23. Correlation (Pearson, Spearman, Kendall) (SOON)
Lecture 24. Causation vs Correlation (SOON)
Lecture 25. Creating insightful visuals (scatterplots, heatmaps) (SOON)
Regression and model thinking
Lecture 26. Simple linear regression (SOON)
Lecture 27. Multiple linear regression (SOON)
Lecture 28. Residual analysis and assumptions (SOON)
Lecture 29. Logistic regression (binary outcomes) (SOON)
Lecture 30. Experimental design and confounding (SOON)