  This article is intended as a guide for new graduate students in the field of
computational science. With the increasing influx of students from diverse
backgrounds joining the ever-popular field, this short guide aims to help
students navigate through the various computational techniques that they are
likely to encounter during their studies. These techniques span from Bash
scripting and scientific programming to machine learning, among other areas.
This paper is divided into ten sections, each introducing a different
computational method. To enhance readability, we have adopted a casual and
instructive tone, and included code snippets where relevant. Please note that
due to the introductory nature of this article, it is not intended to be
exhaustive; instead, we direct readers to a list of references to expand their
knowledge of the techniques discussed within the paper. It is likely that this
article will continue to evolve with time, and as such, we advise readers to
seek the latest version. Finally, readers should note this article serves as an
extension to our student-led seminar series, with additional resources and
videos available at \url{https://computationaltoolkit.github.io/} for
reference.
