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<p></p> <p><b>New page</b></p><div>This page contains some advice for non-CS majors who are interested in programming or have some need for programming in their coursework or research. Note that this advice also applies to CS majors who are working on interdisciplinary projects.<br /> <br /> == Programming Languages ==<br /> The most popular (open-source) programming languages for science and mathematics are currently Python and R. Both are also popular currently because they are used a great deal in data science. There are a great many other languages which have been popular in the past or are still useful in particular situations. But if you are starting out fresh it is likely that Python or R would be your best bet for getting started. Another language/tool of note is GNU Octave, which is an open-source free alternative to MATLAB.<br /> <br /> == Python == <br /> For people new to python (or programming in general) there are many tutorials at [https://wiki.python.org/moin/BeginnersGuide/Programmers python.org&#039;s Beginner&#039;s Tutorial Page].<br /> Python scripts are grouped into &quot;packages&quot; for easy distribution. The most used packages for data analysis are listed here as well as some links to places to learn them:<br /> * numpy: [https://numpy.org/doc/stable/user/absolute_beginners.html Numpy Absolute Beginner&#039;s Guide] <br /> * scipy: [https://docs.scipy.org/doc/scipy/reference/ Scipy Documentation]<br /> * pandas: [https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html 10 Minutes to Pandas]<br /> * matplotlib: [https://matplotlib.org/stable/tutorials/introductory/pyplot.html#sphx-glr-tutorials-introductory-pyplot-py Pyplot Tutorial]<br /> * pytorch: [https://pytorch.org/tutorials/ Tutorials]<br /> <br /> When working with large datasets, the bottleneck is usually around efficiently iterating and structuring through the data. It useful to learn and understand basic Algorithms and Data Structures as well. The information at [https://www.tutorialspoint.com/data_structures_algorithms/index.htm Tutorialspoint] is a good starting point on learning basic Data Structures and Search/Sort algorithms.<br /> <br /> == R == <br /> Mention Bioconductor, R information linked from GH 101 page, R for Data Science<br /> <br /> R is a programming language used primarily by mathematicians and data analysts. It&#039;s used for statistic computing and graphics, though it can be used for data mining as well. R can be used by [https://cran.r-project.org/ command line] or in third party programs like [https://www.rstudio.com/ RStudio]. A good tutorial of the language can be found at [https://www.tutorialspoint.com/r/index.htm Tutorialspoint].<br /> <br /> Other Resources for R:<br /> * [https://www.bioconductor.org/ Bioconductor] (For Computational Biology, Genomes)<br /> * [https://r4ds.had.co.nz/ R for Data Science]<br /> <br /> == Octave ==<br /> Octave is a powerful alternative to MATLAB licensed under the GNU General Public License. It can be downloaded from their [https://www.gnu.org/software/octave/index website]. There is also a [https://wiki.octave.org/Using_Octave tutorial] and [https://octave.org/doc/v6.2.0/index.html#SEC_Contents full documentation] on their site.</div>

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