Course Details

Course Number: 90-760

Management Science II: Decision and Risk Modeling

Units: 6


Ability to work with concepts from probability is required (Binomial and Normal random variables, distributions more generally, computation of mean & standard deviation, event probabilities, Bayes Rule, etc.). Some concepts in 90-760 are easier to grasp if one knows regression, but regression per se is not a prerequisite.
The course uses Excel intensively. If you have not already taken 90-722 or become proficient with Excel in some other way, you should work through some Excel tutorials before the course begins.

Learning Objectives:

Course Objectives:
This course, along with its companions (90-722 Management Science I: Optimization, 94-833 Decision Analysis and Multi-Criteria Decision Making, and 95-760 Decision Making Under Uncertainty) are introductory courses in analytics and management science that survey a variety of hands-on quantitative and modeling methods useful to managers and analysts.

Normally Heinz PPM, HCPM, MBTM, and MEIM students take 90-722 & 90-760, whereas MISM students take 95-760 which pulls examples from the information systems context. Both tracks feed into 94-833, although students with a strong quantitative background can take 94-833 in the first year; none of the other courses are actually a prerequisite for 94-833.

These courses have four objectives, listed in order from least to most important.
First, you should become as comfortable working with spreadsheets, spreadsheet tools, and various add-ins as you already should be with word processors. By the end of the course, firing up Excel to model and solve a quantitative problem should be second nature. This skill will be a significant asset on the job market and in your career.

Second, you should learn about a variety of techniques, what they are capable of, and what their limitations are so that you can intelligently call upon management science specialists and consultants when the occasion arises.

Third, you should acquire sufficient proficiency with some of the techniques that you can use them as an “end user modeler”.

Fourth, you should learn how to approach, abstract, and analyze problems from a quantitative, analytical perspective. In short, you should be able to use the language and perspective of mathematical modeling. In most lectures we will work through a small “case” to help you connect the methods to a problem that is richer than the typical end of chapter problem.

The course moves quickly; be careful not to fall behind. Unless I explicitly say otherwise, always read the assigned readings before the class in which they are discussed; failing to do so is the most common reason for failing the course.


Jonathan P. Caulkins