Statistics Learning Hub

Master probability theory, mathematical statistics, stochastic processes, and time series analysis through comprehensive courses with interactive practice

5 Major Courses100+ Hours ContentAlways Free

Beginner to Advanced

Probability Theory

Master the foundations of probability: random experiments, events, random variables, distributions, and limit theorems

4 modules30-38 hours

Topics covered:

Probability Fundamentals
Random Variables & Distributions
Digital Characteristics
Limit Theorems

Intermediate to Advanced

Mathematical Statistics

Learn statistical inference: distribution families, point estimation, sufficient statistics, confidence intervals, and hypothesis testing

8 modules40-50 hours

Topics covered:

Distribution Families
Point Estimation & Cramér-Rao
Sufficient & Complete Statistics
Confidence Intervals
Hypothesis Testing

Advanced

Stochastic Processes

Master random processes: Markov chains, Poisson processes, Brownian motion, and their applications

6 modules25-30 hours

Topics covered:

Basic Concepts
Markov Chains
Poisson Processes
Brownian Motion
Independent Increment Processes

Intermediate to Advanced

Time Series Analysis

Learn time series decomposition, trend analysis, seasonality, and forecasting methods

5 modules15-20 hours

Topics covered:

Time Series Fundamentals
Trend & Seasonal Decomposition
ARIMA Models
Forecasting Methods

Intermediate to Advanced

Multivariate Analysis

Master multivariate statistical methods: PCA, factor analysis, discriminant analysis, cluster analysis, and canonical correlation

8 modules45-55 hours

Topics covered:

Multivariate Fundamentals
Multivariate Normal Distribution
Principal Component Analysis
Factor Analysis
Discriminant & Cluster Analysis
Canonical Correlation

Recommended Learning Path

Start with Probability Theory to build a solid foundation, then progress to Mathematical Statistics for inference techniques.

1. Probability Theory

→

2. Mathematical Statistics

→

3. Stochastic Processes