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Edward Brown added numerical-analysis.tex
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\subsection{Introduction of numerical analysis}
Students are expected to do their lab work using \ipython\ notebooks. There are several reasons for adopting \texttt{Python}: it is widely used in astronomy and other STEM disciplines; it is easy to learn but supports complex programming; it has powerful libraries---\np, \sp, and \plt---for numerical analysis and plotting; and the \ipython\ notebooks are relatively platform-independent.\footnote{To ensure a standard working environment, we currently have the students install a virtual machine (using \href{https://www.virtualbox.org/}{VirtualBox}) running Ubuntu Linux. Given the widespread availability of \ipython, we will likely drop the usage of virtual machines in subsequent editions of this course.}
Figure~\ref{f.sample-datasets} illustrates an in-class \ipython\ exercise that was used to illustrate the $\chi^2$-test. The students use the notebook to generate fake datsets, to compute $\chi^2$ for those datasets, and then to answer questions about their findings. When finished, the students upload their \ipython\ notebooks to a course dropbox for grading.
