Onsite or via live stream

*Intensive onsite training on data science for finance, quantitative risk modeling and portfolio construction
New York, August 10 - 15 2020*

*The same program as the Quant Bootcamp, delivered in one single online course at your own pace, and enhanced by practice sessions in the Lab*

*In-depth, master-level online program modern quantitative finance in 4 core courses, with emphasis on data science*

*Short math/coding courses to prepare for the Quant Bootcamp, the Quant Core, or the Quant Marathon*

# Body of Knowledge

#### Data Science for Finance

The module "Data Science for Finance" is the largest among the four modules of the ARPM Body of Knowledge.

This module covers the statistical tools needed to model and estimate the joint dynamics of the markets. Unlike related approaches in computer science or engineering, we root our coverage of data science into the pillars of quantitative statistics for finance (the "P" in ARPM). In particular:

- we introduce all machine learning/artificial intelligence models as generalizations of linear factor models, omnipresent (and mis-used) across finance

- we connect the estimation/calibration of all machine learning/artificial intelligence models with classical and Bayesian econometrics

- we address backtesting and model/estimation risk in the context of decision theory

- we translate machine learning/artificial intelligence inference into market view processing: distributional stress-testing for risk management and portfolio/business construction for portfolio management.

This module covers the statistical tools needed to model and estimate the joint dynamics of the markets. Unlike related approaches in computer science or engineering, we root our coverage of data science into the pillars of quantitative statistics for finance (the "P" in ARPM). In particular:

- we introduce all machine learning/artificial intelligence models as generalizations of linear factor models, omnipresent (and mis-used) across finance

- we connect the estimation/calibration of all machine learning/artificial intelligence models with classical and Bayesian econometrics

- we address backtesting and model/estimation risk in the context of decision theory

- we translate machine learning/artificial intelligence inference into market view processing: distributional stress-testing for risk management and portfolio/business construction for portfolio management.

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