Educational repository with R implementations of core Probability Theory concepts,
designed for undergraduate students in Data Analysis. Covers combinatorics, distributions,
conditional probability, random variables, CLT, and applied computational projects.
This repository contains workshop materials for the Probability Theory module
of the Data Analysis course. Materials are used in classroom seminars and include:
- 17 seminar sessions with practical R exercises
- 2 midterm examples with solutions
- 2 exam variants with detailed solutions
- 1 computational project (stock market analysis)
📖 Supplementary materials:
- Course website — games, activities, predictions
- Bilingual Glossary
- Distribution cheat-sheets
- Books, manuals & external resources
| Folder | Contents |
|---|---|
seminars/ |
14 seminar sessions covering the full curriculum |
tests/ |
Exam and midterm variants with solutions (anonymized) |
code/ |
R cheat-sheets: distributions, combinatorics, integration, portfolio analysis |
glossary/ |
Bilingual glossary of probability & statistics terms |
resources/ |
Books, manuals, creative topics, conference info, tool links |
| # | Topic |
|---|---|
| 04 | Event Algebra |
| 05 | Introduction to Probability |
| 06 | Combinatorics |
| 06a | Geometric Probability |
| 07 | Conditional Probability & Bayes |
| 08 | PMF & CDF |
| 09 | Bernoulli & Binomial Distributions |
| 10 | Hypergeometric & Poisson Distributions |
| 11 | Covariance and Correlation |
| 12 | Continuous Random Variables |
| 14 | Jointly Distributed Discrete RVs |
| 17 | Central Limit Theorem |
# Example: Binomial distribution visualization from Seminar 9
n <- 20
p <- 0.4
x <- 0:n
prob <- dbinom(x, size = n, prob = p)
barplot(prob, names.arg = x, col = "steelblue",
main = "Binomial Distribution (n=20, p=0.4)",
xlab = "Number of successes", ylab = "Probability")- R — statistical computing and graphics
- Base R graphics — distribution visualizations
- GitKraken — version control
These materials support the Data Analysis course.
The course combines theoretical foundations with hands-on R programming.
🔗 External resources:
- Interactive course site — games, forms, predictions
- Probability & Game Theory Glossary — bilingual terms (EN/RU)
- R Code Reference — distributions, Excel functions, portfolio formulas
For questions about the course materials, please reach out via GitHub Issues
or visit vladova.ru.