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Ali Habibnia's Projects

algorithmic_trading_with_python icon algorithmic_trading_with_python

This comprehensive, hands-on course provides a thorough exploration into the world of algorithmic trading, aimed at students, professionals, and enthusiasts with a basic understanding of Python programming and financial markets.

cmda_4984_data_science_for_quantitative_finance icon cmda_4984_data_science_for_quantitative_finance

This course in applied data science covers the theoretical foundations of advanced quantitative approaches in machine learning, econometrics, risk and portfolio management, algorithmic trading, and financial forecasting. (first taught at Virginia Tech in 2019)

econ_5314g_big_data_economics icon econ_5314g_big_data_economics

This intermediate applied econometrics course covers the theoretical, computational, and statistical underpinnings of the big data analysis. (first taught at Virginia Tech in 2018)

machine-learning-from-theory-to-practice icon machine-learning-from-theory-to-practice

This course will introduce the student to classic machine learning algorithms and deep neural network structures. The style will be first to describe the theory and math behind algorithms and then demonstrate how to use Python to create and run the models.

pca_nl_test icon pca_nl_test

A Nonlinearity Test for Principal Component Analysis: MATLAB Code

quantum-computing-solutions-for-econometrics icon quantum-computing-solutions-for-econometrics

In this project, we delve into the principal constructs of quantum computing and quantum machine learning. Our primary focus resides in identifying and elucidating quantum computing solutions for the domain of econometric modeling, with particular emphasis on big data econometrics and nonlinear models.

statistical-dependence-the-history-and-new-trends icon statistical-dependence-the-history-and-new-trends

"The History and New Trends of Measuring Dependence: From Bayes, Galton, and Pearson to the 21st Century" is a research undertaking led by Ali Habibnia as the principal investigator, with the assistance of Jonathan Gendron and Sanjana Rayani, who serve as research assistants from the Department of Economics at Virginia Tech.

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