Physics 391 | Experimental Data Analysis Lab
Instructor: Dr. Elsa Johnson
About this Course
Our society is becoming more and more data driven and to work in the real world one needs some proficiency in programming, statistics and data analysis. The goal of this course is to introduce you to these topics through hands on experience in physics and data labs. This course by no means is comprehensive. Instead it highlights some of the more important tools that you will see again if you pursue science, business or any kind of data related job (e.g. twitter, google, facebook, etc). We will focus more on the practical aspects of error analysis, linear regression, simple model fitting and if time, distributions. Theory and derivation of these techniques will be briefly discussed if necessary with the idea that you can research these topics in depth at a later time if so desired. If you don’t know how to program, you will learn enough python at the beginning of the course to get you through the labs.
3/31/2015: Week 1
Measurement Uncertainties, Intro to Scientific Programming Tasks
- Homework 1: HW1 Due 4/10 at Noon in Wil 417
- Lab 1: Programming practice
- Pre-lab: If you have never programmed before, do the python tutorial at Codecademy
- Lab document: pdf Due 4/16
- data file for lab 1
- Reading: Taylor Ch. 1-3
4/7/2015: Week 2
More on error propagation; uncertainty as standard deviation
- Homework 2: HW2 Due 4/21
- Reading: Taylor Ch. 4
4/14/2015: Week 3
Stellar Astronomy
4/21/2015: Week 4
Weighted averages and more astronomy
- Homework 3: HW3 Due 5/5
- Reading: Taylor Ch. 7
- Lecture: Astronomy
4/28/2015: Week 5
Linear Regression and Brownian motion
Prelab for lab 3 Due 5/1
Lab 3: Brownian Motion Due 5/14
Reading: Taylor Ch. 8
5/5/2015: Week 6
Binomial Distribution and Random Walks
- Homework 4: HW4 Due 5/19
- Reading: Taylor Ch. 9-10
5/12/2015: Week 7
Fourier Transforms and Poisson Distribution
5/19/2015: Week 8
Fourier Transforms II
5/26/2015: Week 9
Climate Change Data
6/2/2015: Week 10
Features in Data Sets