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RISK ANALYTICS

ACTSCI 657
Course Description

Develop a toolbox for modeling, communicating, and managing risk and uncertainty in business applications. Emphasis on the notation of probabilistic forecasting and introduces a predictive modeling framework that integrate modern machine learning methods with distribution-based regression models. Topics include heavy-tailed regression, count data regression, survival data analysis, feature engineering using neural networks and natural language processing, among others.

Prerequisites

(ACTSCI 640 , GENBUS 656 , STAT 333 , or STAT 340 ), or declared in undergraduate Business Exchange program

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.6

1.62% from Historical

Completion Rate
100%

No change from Historical

A Rate
47.06%

20.78% from Historical

Class Size
34

32.47% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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