Meetings: Tuesdays and Thursdays, 11:30-12:45, HH 1000
Website: http://jfukuyama.github.io/teaching/stat610
| Instructor: Prof. Julia Fukuyama | jfukuyam at iu dot edu | |
| Office hours: Thursdays 2-4pm | ||
| Associate Instructor: Mr. John Koo | johnkoo at iu dot edu | |
| Office hours: | ||
| Mondays 4-6pm |
As a statistician, you will need to manipulate data, optimize, and simulate. You will also need to know enough about how the methods you use work to diagnose problems when they arise and to be able to implement modified versions when the standard implementations don’t suit your purposes.
You also need to write accurate, clean, maintainable, demonstrably correct code. To that end, the first half of the class will be devoted to how to program well, with statistical tasks giving us the computational problems.
Once we have the software engineering down, we will move on to the algorithms used in applied statistics. These can be roughly broken up into optimization methods and stochastic simulation methods.
The primary textbook for the first half of the course with be The Art of R Programming, by Norman Matloff.
The R Cookbook, by Paul Teetor, will also be useful.
The primary textbook for the second half of the course will be Numerical Analysis for Statisticians, by Kenneth Lange.
Additional readings will be posted on the course website.
Assessment will be based on a combination of homework, an in-class midterm, and a final project. Final grades will be based on:
Participation will have an idiosyncratic meaning this semester, which we will figure out together.
There will be 8 homeworks over the course of the semester, generally graded out of 5 points, with one point for a good-faith effort at all the problems, 5 points for correct answers with clean code, and an intermediate number of points otherwise.
Homeworks will be assigned on Sundays and due the following Tuesday (9 days later). At the time the homework is assigned, we will generally not have covered all the material needed to complete the homework, but we will have covered everything by the Thursday before the due date. The idea is to give you the homework early enough that you can think about it while the material is being covered in lecture. Therefore, it will generally be a good idea to take a look at the homework when it is assigned even if you aren’t able to complete all the problems yet.