Difference between revisions of "CHEM-6111"
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* Advanced Data Analysis (Igor, 4 weeks) | * Advanced Data Analysis (Igor, 4 weeks) | ||
| + | ** Numerical solution of ODEs | ||
** convolution (student request to IMU) | ** convolution (student request to IMU) | ||
| − | ** | + | ** Montecarlo simulations |
| + | ** Positive Matrix Factorization | ||
** Frequency analysis and FFT | ** Frequency analysis and FFT | ||
| − | |||
| − | |||
* Data Acquisition (Labview, 3 weeks) | * Data Acquisition (Labview, 3 weeks) | ||
Revision as of 13:30, 17 August 2012
Special Topics - Data Analysis and Acquisition - Tentative Outline
This 3-credit course will be offered by Dr. Ingrid Ulbrich (with assistance from Prof. Jose L. Jimenez) in Fall of 2012. It will be a required course for 1st year analytical chemistry students, and optional for others. The course number may be special topics, depending on whether CU can approve the new course number by then.
- Introduction to computer graphing, functions and programming (Igor, 2 weeks)
- Basic Data Analysis (Igor, 6 weeks)
- propagation of errors
- Basic analysis: interpolation, area integrals, multidimensional data handling
- Statistics: calculating statistical parameters, distributions, precision, uncertainty, ANOVAA
- Correlation and regressions (variants)
- centered (Pearson's) vs uncentered correlation
- vertical vs orthogonal distance regression
- squared vs absolute value errors for regression
- weighted vs unweighted regression
- boxcar and weighted smoothing
- Signal-to-noise, noise reducing measures, signal enhancing measures, numerical high pass filters, low pass filters etc.
- Fitting of custom functions
- Advanced Data Analysis (Igor, 4 weeks)
- Numerical solution of ODEs
- convolution (student request to IMU)
- Montecarlo simulations
- Positive Matrix Factorization
- Frequency analysis and FFT
- Data Acquisition (Labview, 3 weeks)
- Fundamentals of data acquisition
- Data I/O, parsing of text files, write to file etc.
- Labview: introduction and programming
- Data acquisition problems brought by students from their research
- Potential Texts
- Numerical Recipes (online)
- Building Scientific Apparatus (Moore, Davis, and Coplan)
- Taylor Error Analysis
- Stuff from Amber's stats class
