aoml/python_prep_guidelines.md

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
  • Creating new columns through operations on existing 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
• A brief video intro to Colab ( Spanish): https://www.youtube.com/watch?v=8VFYs3Ot_aA
• Another brief video intro to Colab ( English): https://www.youtube.com/watch?v=oCngVVBSsmA
• A free, 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