This course provides a comprehensive introduction to statistical modeling techniques in financial engineering. Students will learn to apply statistical tools, such as exploratory data analysis, distributional modeling, maximum likelihood estimation, and statistical inference, to financial datasets. The course also covers advanced methods for financial data modeling, including resampling (Bootstrap), copula models and time-series analysis (ARIMA and GARCH). Practical Python sessions give students hands-on experience analyzing real financial datasets, estimating and validating models, and addressing the challenges of modeling in complex financial markets.