Core Area Courses (Option 1)

Track Option 1 only

This listing of Core Area Courses is applicable only for those going through the Straight PhD program under the Program Tracks. Other pages of interest may be as follows.

• Common Courses

• Elective Courses

Besides the DS 301: Foundations of Data Science as a core course under the program, at least one three-unit pre-existing graduate course selected from each of the following core areas is also required.

Mathematical Methods Area

Any of the following courses may be taken and credited under Mathematical Methods.

1. Math 271.1 (Numerical Analysis I) [prerequisite: Math 171/COI]

2. AI 211 Computational Linear Algebra [prerequisite: COI]

3. Stat 207 (Statistical Inference for Data Science) [prereq: Stat 195/equiv., Stat 100/equiv.]

4. Stat 280 (Special Fields of Statistics) [prereq: COI; must be about topics in mathematical statistics]

Computational Methods Area

Any of the following courses may be taken and credited under Computational Methods.

1. Physics 215 (Advanced Computational Methods of Physics) [prerequisite: App Phys 155/equiv.]

2. Stat 217 (Computational Statistics) [prereq: Stat 207/equiv.; coreq: Stat 208/equiv.]

3. Stat 280 (Special Fields of Statistics) [prereq: COI, must be about topics in Computational Methods]

4. CS 236 Scientific Computing [prereq: COI]

5. IE 271 Information Systems Engineering [prereq: COI]

Statistical Methods Area

Any of the following courses may be taken and credited under Statistical Methods.

1. IE 211 Quantitative Methods in Eng’g [prereq: none]

2. IE 230 Statistical Design and Analysis for Eng’g [prereq: IE 28/equiv.]

3. Any graduate-level statistics course prescribed by the Program Adviser.