Decision Science (D S)
D S 119S, 219S, 319S, 419S, 519S, 619S, 719S, 819S, 919S Topics in Decision Science 1-9 Hours
0 Lecture Hours 0 Lab Hours
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad advisor in the academic unit. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Lower Division
D S 129S, 229S, 329S, 429S, 529S, 629S, 729S, 829S, 929S Topics in Decision Science 1-9 Hours
0 Lecture Hours 0 Lab Hours
This course is used to record credit the student earns while enrolled at another institution in a program administered by the University's Study Abroad Office. Credit is recorded as assigned by the study abroad advisor in the academic unit. University credit is awarded for work in an exchange program; it may be counted as coursework taken in residence. Transfer credit is awarded for work in an affiliated studies program.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
D S 235 Introduction to Decision Science 2 Hours
2 Lecture Hours 0 Lab Hours
Examine modeling of business problems using methods from decision analysis, simulation and optimization. Decision Science 235 and 235H may not both be counted. Two lecture hours a week for one semester.
Pre/Corequisites: Statistics 301 or 301H; Mathematics 408Q or credit or registration for Mathematics 408D, 408L, or 408S.
Restrictions: Restricted to students in the McCombs School of Business.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Upper Division
D S 235H Introduction to Decision Science: Honors 2 Hours
2 Lecture Hours 0 Lab Hours
Examine modeling of business problems using methods from decision analysis, simulation and optimization. Decision Science 235 and 235H may not both be counted. Two lecture hours a week for one semester.
Pre/Corequisites: Statistics 301, 301H, or Statistics and Data Science 321; Mathematics 408Q or credit or registration for Mathematics 408D, 408L, or 408S.
Restrictions: Restricted to students in the McCombs School of Business Honors Program.
Grading: Letter Grade
Repeatable for credit: No
Academic Level: Upper Division
D S 372T Topics in Decision Science 3 Hours
3 Lecture Hours 0 Lab Hours
Three lecture hours a week for one semester.
Pre/Corequisites: Varies with the topic.
Restrictions: Restricted to students in a business major.
Grading: Student Option
Repeatable for credit: May be repeated for credit when the topics vary.
Academic Level: Upper Division
Topic 11: Computational Finance. Introduction to the analysis and implementation of numerical methods used in finance. Explore numerical techniques in derivative pricing and optimal asset allocation, such as Monte Carlo and quasi-Monte Carlo simulation, methods for solving partial differential equations, and dynamic programming. Only one of the following may be counted: Decision Science 372 (Topic 7), 372T (Topic 11), Statistics 372 (Topic 7), 372T (Topic 11). Same as: STA 372T.11
Topic 16: Optimization Method in Finance. Explore quantitative methods and techniques in optimization and simulation, and their use in financial decision making. Discuss theory and application in portfolio selection, options and other derivative pricing, index tracking, risk measures, volatility estimating. Examine linear, quadratic, nonlinear, and integer programming; dynamic programming; robust optimization; Monte Carlo methods and variance reduction techniques. Emphasis will be placed on problem solving with advanced computational programming languages. Only one of the following may be counted: Business Analytics 372 (Topic 6), 372T (Topic 16), Decision Science 372 (Topic 6), 372T (Topic 16), Finance 372 (Topic: Optimization Method Finance), 372 (Topic 6), 372T (Topic 16), Statistics 372 (Topic 6), 372T (Topic 16). Same as: BAX 372T.16, FIN 372T.16, STA 372T.16
D S 372T.11 Computational Finance 3 Hours
3 Lecture Hours 0 Lab Hours
Introduction to the analysis and implementation of numerical methods used in finance. Explore numerical techniques in derivative pricing and optimal asset allocation, such as Monte Carlo and quasi-Monte Carlo simulation, methods for solving partial differential equations, and dynamic programming. Only one of the following may be counted: Decision Science 372 (Topic 7), 372T (Topic 11), Statistics 372 (Topic 7), 372T (Topic 11).
Same as: STA 372T.11
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division
D S 372T.16 Optimization Method in Finance 3 Hours
3 Lecture Hours 0 Lab Hours
Explore quantitative methods and techniques in optimization and simulation, and their use in financial decision making. Discuss theory and application in portfolio selection, options and other derivative pricing, index tracking, risk measures, volatility estimating. Examine linear, quadratic, nonlinear, and integer programming; dynamic programming; robust optimization; Monte Carlo methods and variance reduction techniques. Emphasis will be placed on problem solving with advanced computational programming languages. Only one of the following may be counted: Business Analytics 372 (Topic 6), 372T (Topic 16), Decision Science 372 (Topic 6), 372T (Topic 16), Finance 372 (Topic: Optimization Method Finance), 372 (Topic 6), 372T (Topic 16), Statistics 372 (Topic 6), 372T (Topic 16).
Same as: BAX 372T.16, FIN 372T.16, STA 372T.16
Grading: Student Option
Repeatable for credit: No
Academic Level: Upper Division