Analyze style and factor exposures of portfolios

Implement robust estimates for the covariance matrix

Implement Black-Litterman portfolio construction analysis

Implement a variety of robust portfolio construction models

Learn the principles of supervised and unsupervised machine learning techniques to financial data sets

Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes

Utilize powerful Python libraries to implement machine learning algorithms in case studies

Learn about factor models and regime switching models and their use in investment management

Learn what alternative data is and how it is used in financial market applications. 

Become immersed in current academic and practitioner state-of-the-art research pertaining to alternative data applications.

Perform data analysis of real-world alternative datasets using Python.

Gain an understanding and hands-on experience in data analytics, visualization and quantitative modeling applied to alternative data in finance