Python prep guidelines.

python_prep_guidelines.md

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+# Python prep guidelines
+
+On lecture 1, I informed you that you will be writing Python code as part of
+the 3 cases you will need to go through in this course. This means that, the
+more you know about Python, the less you will need to focus on *how* to do
+things with Python and the *more* you will be able to focus on actually solving
+the case.
+
+This preparation is specially important if you have never done any coding. I
+highly advise you to take your time to work your Python skills out before case
+1 begins. It will pay off down the line.
+
+This document contains two sections: a summary of the skills you are looking
+for, and a few suggested contents that can help you. You don't need to use
+suggested contents: if you find others that you prefer, go ahead. I do
+recommend that you stick to trying to learn the skills I am suggesting, since
+that will maximize the return on your effort. There are many other areas of
+Python that you can learn but won't be relevant for this course.
+
+## Skills to look for
+
+- Notebooks and Colab
+    - How to use your UPF gmail to open
+      Colab (https://colab.research.google.com)
+    - How to create a new notebook, write code in it and run it
+    - How to upload and download CSV or Excel files in Colab notebooks
+- Python basics
+    - Variables
+    - Functions
+    - Basic data types: ints, floats, strings
+    - Complex data types: lists, dicts, tuples
+- Pandas
+    - Understanding pandas dataframes
+    - How to read and write CSV/Excel files as dataframes
+    - How to select data
+        - Selecting columns
+        - Selecting rows with conditions
+    - Grouping and aggregating dataframes
+    - Obtaining statistics from columns
+- Seaborn
+    - Understaning seaborn plots
+    - How to plot data from a pandas dataframe with seaborn
+
+## Suggested Materials
+
+- A super brief Colab example
+  notebook: https://colab.research.google.com/?utm_source=scs-index
+- A more detailed, high quality example Colab
+  notebook: https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb
+- https://www.youtube.com/watch?v=8VFYs3Ot_aA
+- https://www.youtube.com/watch?v=oCngVVBSsmA
+- A short, 4 hours intro to Python from
+  Datacamp: https://www.datacamp.com/courses/intro-to-python-for-data-science
+- A self-pace introduction to Python for STEM
+  applications: https://www.pythonlikeyoumeanit.com/index.html
+- A huge collection of resources, in case you don't like the previous ones or
+  want to find more: https://www.reddit.com/r/learnpython/wiki/index