Dates: February 25, March 3 and 14, 2016, intensive workshop (3 full days)
Time: 9 am - 17 pm
Location: SBII (03-432), Colonel-Kleinmann-Weg 2
Lecturer: Dr. Kirsten Winkel
Course Description
The course “Computational Tools for Data Analysis” is designed to give MAINZ graduate students fluency in handling their measurement data. We will get familiar with MATLAB as a powerful tool for processing, analyzing, and visualizing data. Where appropriate, other programs will be involved in this course, e.g. Excel, Origin, LabVIEW, Mathematica, and image processing programs. To meet the needs of participants with different backgrounds, we will alternate lecture and practice phases of varying difficulty.
Requirements
This course is addressed to MAINZ graduate students with low to medium experiences in data processing. The course is limited to 16 participants. Please subscribe online by Dec. 21, 2015. PhD and Master students that are not affiliated with MAINZ may also register but preference will be given to MAINZ students.
Topics Overview
February 25, 2016
Overview of various tools for data analysis
Starting with MATLAB fundamentals and the user interface
Data handling: Vectors, matrices and tensors
Data processing: Control structures, scripts and functions in MATLAB
March, 3, 2016
Data analysis: Basic statistics, curve fitting, smoothing, interpolation, fourier transformation
Data visualization: 2D- and 3D-plots, scaled images
Advanced exercises and problem sets
GUI programming, debugging and programming strategies for systematic data analysis
March 14, 2016
Application examples from individual MAINZ working groups
Special topics (depending on participants’ needs, e. g. image processing, signal processing, advanced
programming excercises, programming graphical user interfaces, LabVIEW or other data analysis tools…)
Contact
Dr. Kirsten Winkel (kirsten.winkel@uni-mainz.de, 06131 39 22034)
Consultation time: After each course session and every Friday, consultation time is offered in room 03-231, Staudinger Weg 9
The participants get support for implementing and applying the introduced tools to their own problems.