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