# C6 Major Requirement (66 credit units)

The program does not require the specification of a concentration area. However, some students will find it to be a useful option to build their program around specific courses with applications in particular areas. For example, students with an interest in the physical sciences (chemistry, geology and geophysics, physics, engineering) might choose to emphasize ‘continuous’ mathematical tools such as differential equations, whereas students with interests in the life sciences (biology, biochemistry, biomedicine, biostatistics, epidemiology) and the social sciences (economics, psychology, sociology) might direct their study towards ‘discrete’ mathematical methods such as linear programming, graph theory, combinatorics or towards statistical methods.

Ideally, by the time of graduation, every student will have been exposed to aspects of continuous, discrete, and probabilistic and statistical methods.

- MATH 211.3
- MATH 238.3
- MATH 313.3
- MATH 327.3
- MATH 331.3
- MATH 336.3
- MATH 339.3
- MATH 352.3
- MATH 366.3
- MATH 371.3
- MATH 379.3
- MATH 436.3
- MATH 438.3
- STAT 241.3

Choose **3 credit units** from the following:

- MATH 164.3 (formerly MATH 264.3)
- MATH 266.3

Choose **6 credit units** from the following:

- MATH 223.3 and MATH 224.3
- MATH 225.3 and MATH 226.3
- MATH 276.3 and MATH 277.3

Choose **6 credit units** from the following:

- MATH 328.3
- MATH 361.3
- MATH 373.3
- STAT 242.3
- STAT 341.3

Choose **3 credit units** from the following:

- MATH 314.3
- CMPT 394.3

Choose **6 credit units** from the following:

- MATH 433.3
- MATH 439.3
- MATH 452.3
- MATH 465.3
- MATH 485.3
- MATH 498.3 (Special Topics in Applied Mathematics)
- STAT 442.3
- STAT 443.3

See suggested sequence of courses below to see sample concentrations. NOTE: Students may choose to follow a sample program to focus in a specific area, but are not required to do so.

Sample program #1 – Continuous Modelling and Differential EquationsSample program #2 – Discrete Modelling

Sample program #3 – Probabilistic and Statistical Modelling