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\subsection{Introduction of numerical analysis}  Students do their lab work using \ipython\ notebooks. There are several reasons for adopting Python: it is widely used in astronomy and other STEM disciplines; it is easy to learn but it supports complex programming; it has powerful libraries---\href{http://www.numpy.org/}{numpy}, \href{http://www.scipy.org/}{scipy}, and \href{http://matplotlib.org/}{matplotlib}---for numerical analysis and plotting; and the \ipython\ notebooks are relatively platform-independent.\footnote{To ensure a standard working environment, we require the students to install (using \href{https://www.virtualbox.org/}{VirtualBox}) a virtual machine running Ubuntu Linux with the Anaconda Python \href{https://store.continuum.io/cshop/anaconda/}{Anaconda Python}  distribution. Given the broad cross-platform distribution of Anaconda Python, \href{https://store.continuum.io/cshop/anaconda/}{Anaconda Python},  we may drop the usage of virtual machines in subsequent editions of this course.} Figure~\ref{fig:sample-datasets} illustrates an in-class exercise, using an \ipython\ notebook, on 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 notebooks to a course dropbox for grading. The notebook used to make Fig.~\ref{fig:sample-datasets} is available from the online version of this document. To try the notebook, move the cursor over the figure and click on the \verb|Launch Ipython| link that appears below the figure. Occasionally the page fails to load if the server is busy; if that happens, simply reload the page.