An Introduction to Data Analysis
The introduction to data analysis by Daniel Reardon demonstrates measurements and uncertainties, and how they relate to probability density functions. The lecture explores how to fit models to a series of measurements (a data set), quantify model parameter uncertainties, and test the quality of a model fit. The lecture ends with a brief introduction into the ideas of least-squares model fitting, Bayesian inference, and model selection, which are concepts used in the pulsar timing software explored in the later lectures.
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Exercises
The data and exercises for all workshop lectures can be downloaded (and extracted) to the shared directory in your virtual machine as described here.
Within the downloaded /home/pulsar/SHARE/exercises/lec4_data_analysis directory on your virtual machine you will find the jupyter notebook data_analysis_model_fitting.ipynb which will introduce you to data analysis methods using python commands.
Launch the notebook by running,
jupyter notebook data_analysis_model_fitting.ipynb
which will automatically launch the notebook in a browser in your virtual machine.
Note: this exercise does not yet rely on having pulsar software installed. If you have the relevant python modules installed you may also be able to run it outside of the virtual machine.
