Lab 5 due Friday June 12th by noon!

Instructor Prof. Eric Torrence Willamette 418, 346-4618
torrence (at) uoregon
Office Hours: F 2-4
Lab
Assistant		 Kathy Hadley
Sequoia Alba		 khadley (at) uoregon
salba (at) uoregon
Lecture MW 12:00-1:20 Willamette 112
Labs Lab schedule
Textbook Introduction to Error Analysis, 2ed, Taylor
Getting Started with MATLAB 7, Pratap

Overview

This course will introduce the basic concepts of data analysis and practical techniques for implementing them. Half of the course will emphasize the theoretical foundation of data analysis with lectures and homework assignments, while the other half will emphasize the practical application of data analysis in lab assignments. Development of programming techniques for performing data analysis and data visualization will also be stressed, using MATLAB. The following topics will be covered:

Grading

Course grades will be based on five bi-weekly homework assignments from Taylor (50%), and five bi-weekly lab assignments (50%). There will be no examinations (midterm or final) for this course, so it is important for students to turn in all assigned work when it is due. Late assignments will either be penalized or not accepted at the instructors discretion. The final lab assignment will be due during finals week in place of a final examination.
In order to pass the course, you must complete all of the labs!

Syllabus

Week Topic Lab
(due Monday)		 Homework
(due Monday)		 Reading
Week 1
3/30 - 4/3		 Measurement Uncertainties Lab Signup HW1 Taylor Ch. 1-3
Pratap Ch. 1-2
Week 2
4/6 - 4/10		 Statistical Inference MATLAB Intro Taylor Ch. 4
Pratap Ch. 1-2
Week 3
4/13 - 4/17		 Normal Distribution HW2 Taylor Ch. 5
Pratap Ch. 3-4
Week 4
4/20 - 4/24		 Monte Carlo Techniques Calculation of Pi Taylor Ch. 6-7
Pratap Ch. 3-4
Week 5
4/27 - 5/1		 Linear Regression HW3 Taylor Ch. 8
Pratap Ch. 5-6 (as needed)
Week 6
5/4 - 5/8		 Binomial Distribution and Random Walks Brownian Motion Taylor Ch. 10
Pratap Ch. 5-6 (as needed)
Week 7
5/11 - 5/15		 Poisson Distribution and Counting Statistics HW4 Taylor Ch. 11
Week 8
5/18 - 5/22		 Random Processes Photon Counting (due 5/27)
Week 9
5/25 - 5/29		 Fourier Transforms
No Class Monday		 HW5 (due 6/3) FFT Handout
Week 10
6/1 - 6/5		 Fourier Transforms Fourier Transforms (due 6/12)
Finals
6/8 - 6/12		 Final Lab Due Friday (noon)

This syllabus is tentative, and is subject to change as the quarter progresses.

MATLAB

One of the goals of this course is to give you the skills to properly do non-trivial data analysis of large data samples. There are many different tools available to do this, but we had to pick something, and we are going to use MATLAB. MATLAB is relatively easy to learn, very powerful, and widely used in Science and Engineering diciplines. MATLAB is also a good example of 'procedural programming', and the general techniques learned in this course can easily be transferred to the language or tool of your choice later. MATLAB is widely (and freely) available at the University of Oregon. Some possibilities are listed on the MATLAB Information page. You will need to use MATLAB extensively in this course, so you should invest some time in the first two weeks to make sure you have a working computing environment which you can use and you are happy with.

Homework

Homework will typically be assigned every other week on Monday and due on the following Monday at the start of class. The homework will mostly be problems from Taylor forcing you to work through a particular concept 'by hand' at least once. Supplimental problems to exercise your MATLAB skills may also be assigned.

Labs

Lab assignments will be made every two weeks and will be due on Monday during weeks when homework is not due. It is expected that you will work on your labs during the two weeks before they are due. Assigned lab times will be available when TAs will be in room 17 to provide support and advice, although you are free to work on the labs whenever you have time available. You will be expected to work with a partner, although each member of the lab group is expected to turn in their own material including the data analysis and any associated code.

Formal writeups will not be required, although I do expect you to keep a lab notebook which clearly shows the work you have done. I really want to see proof that you did the lab and understood the material. Neatly organized notes taken during the lab itself, answers to the questions posed in the lab writeup, plus a short summary giving the main quantitative results in your notebook is perfectly adequate. If you are very sloppy in your notes, you may also turn in a longer printed write up, but please get into the habit of taking neat legible lab notes. Either way, please turn in your lab notebook (legible or not) on Monday in class. For labs with significant computer work, your M-files and supplimental material should be emailed to me directly as well.