The purpose of the Data Analysis course is to provide an
introduction to a variety of concepts and techniques used in
interpreting atmospheric and ocean measurements and numerical
model output, with an emphasis on time
series. In most cases, applying these
techniques requires computing, hence
at least minimal programming. This raises the question of
which computer language and other software to use.
For this course we have chosen Python as the language. It
is widely used in almost all applications of computing
(e.g., Facebook and Google are major users of Python), and
it is gaining prominance in many areas of science, including
biology (especially neuroscience and bioinformatics) and the
earth sciences (with atmospheric science being perhaps the
most rapid adopter). Do you want to verify the LIGO
gravitational wave discovery? It's all
`here `_
in Python.
If you will need to do some programming as a student and/or as a
professional, a moderate amount of time spent learning good
tools and practices at an early stage will pay off ever
after. By working in Python with both simulated and real data
sets, we will try to develop computing skill along
with an understanding of basic statistics and data analysis
techniques. Along the way we will introduce tools such as
Distributed Version Control Systems (Mercurial and git) and
the Python `Sphinx `_ web-site
generator used to make this site.