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Python for Earth Science Students
¶
Lecture 1
¶
Learn basic notebook anatomy.
Learn how to launch a Jupyter notebook.
Learn some basic python operating system commands.
Lecture 2
¶
Learn about variables.
Learn about operations.
Lecture 3
¶
Learn about collections of variables: data structures.
Learn about Python objects.
Learn about methods which allow you to do things to Python objects.
Lecture 4
¶
Learn more about another useful data structure, dictionaries and some of their methods.
Introduce special Python code blocks.
Learn about
for
loops,
while
loops and
if
blocks.
Lecture 5
¶
Learn about functions.
Discover the joys of modules.
Lecture 6
¶
Introduction to the very useful Python packages called
NumPy
and
matplotlib
.
Lecture 7
¶
Learn more about
NumPy
and
matplotlib
.
Learn more about
NumPy
arrays.
Lecture 8
¶
More about
matplotlib
: adding notes and saving images.
Learn about DataFrames and Series, two new data structures, that are part of the
Pandas
package.
Learn some basic filtering tricks with
Pandas
.
Learn how to read in and save data files with
Pandas
.
Lecture 9
¶
Learn how to filter data with
Pandas
.
Write a program to calculate the great circle distances between two known points.
Learn how to generate formatted strings for output.
Lecture 10
¶
Learn about object oriented programming (OOP).
Learn how to create a Python
class
.
Learn more about namespaces.
Learn more about copies.
Lecture 11
¶
Learn about
lambda
functions.
Explore the joys of
list
,
set
and
dict
(dictionary) comprehension.
Learn about exception handling.
Lecture 12
¶
Tricks with
Pandas
.
Filtering.
Concatentating and merging dataframes.
Lecture 13
¶
Learn a few more
Pandas
tricks.
Learn how to make more complicated plots with
matplotlib
.
Learn about the composition of the sun, solar system and Earth.
Lecture 14
¶
Learn how to plot histograms and cumulative distributions.
Learn how to get lists of random numbers.
Learn about the topography of the Earth (hypsometric curve).
Lecture 15
¶
Learn some basic statisics - samples versus populations and empirical versus theorectical distributions.
Learn to calculate central tendencies, spreads.
Learn about significant figures and more about formatting output.
Learn some useful functions in
NumPy
and
SciPy
for simulating distributions and calculating statistics.
Lecture 16
¶
Learn how to deal with bivariate data (fitting lines, curves).
Apply line fitting to determine the age of the Universe.
Lecture 17
¶
Start to make some basic maps using
CartoPy
.
Lecture 18
¶
Learn about
geoplot
and
geopandas
.
Learn a bit about coordinate systems (UTM versus WGS84, as examples).
Learn something about Hawaiian volcanism.
Lecture 19
¶
We will work with directional data using rose diagrams and stereonets
Lecture 20
¶
Learn some useful tricks about matrix math.
Lecture 21
¶
Learn how to plot great and small circles on an equal area net and map projections.
Lecture 22
¶
Find out about Machine Learning.
Learn about using the
scikit-learn
python package for clustering analysis.
Apply clustering analysis to Earth Science problems.
Lecture 23
¶
Learn how to use satellite imagery to understand land usage.
Learn how to use patches in
matplotlib
.
Learn about using the
scikit-learn
Python package to classify data.
Lecture 24
¶
Learn how to perform image compression via singular value decomposition.
Learn about Principal Component Analysis (PCA).
Lecture 25
¶
Learn about gridding and contouring with
CartoPy
.
Lecture 26
¶
Learn about 3D plots of points and surfaces.
Show some examples with subduction zone earthquakes and isotopic systems.
Lecture 27
¶
Learn how to load NetCDF files.
Learn about analysis techniques you can conduct with a digital elevation model.
Lecture 28
¶
Learn about the diffusion equation.
Lean about landscape evolution models.
Lecture 29
¶
Learn how to make and display animated gifs.
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