Information Engineering

Probability & Statistics

Core probability theory and statistical methods implemented in Python — from distributions and hypothesis testing to Bayesian inference.

1. Probability Space
2. Random Variables
3. Expectation and Variance
4. Conditional Probability and Bayes' Theorem
5. Law of Large Numbers and Central Limit Theorem
6. Stochastic Processes

Machine Learning

From linear models to deep learning — theory, derivation, and implementation of fundamental machine learning algorithms.

Coming soon

Financial Engineering

Quantitative finance methods — pricing, risk measures, stochastic processes, and portfolio optimization.

Coming soon

Biostatistics

Statistical methods for life sciences — survival analysis, clinical trial design, and epidemiological modeling.

Coming soon

Financial Risk

Credit scoring, PD/LGD/EAD modeling, portfolio credit risk, and regulatory capital calculation.

Coming soon

Banking Regulation

Basel framework implementation — capital adequacy, stress testing, and regulatory reporting.

Coming soon

Accounting

Quantitative approaches to accounting problems — IFRS 9 expected credit loss, fair value measurement, and financial analysis.

Coming soon

Social Issues

Data-driven analysis of social challenges — inequality, public health, labor markets, and policy evaluation.

Coming soon