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bootcamps

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SQL section 3

eerikkemperman committed 8 years ago
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SQL section 2

eerikkemperman committed 8 years ago
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SQL section 1

eerikkemperman committed 8 years ago
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No need for ipython-sql.zip, pip install works with --user

eerikkemperman committed 8 years ago
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More changes

ggovertbuijs committed 8 years ago
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Split Game 3

ggovertbuijs committed 8 years ago

README

The README file for this repository.

Python bootcamps (EUR)

This Git repository contains the notebooks to be used for the Python bootcamps at the Erasmus University Rotterdam.

Background

  • We want to introduce programming, using Python, to PhD students.
  • We want to establish an EUR Python community in order to be able to share knowledge and to provide a distributed form of support for members of the community.
  • Use a public repository to store the contents of the course.

Instruments used

  • Anaconda Python distribution (Python 3.5)
  • Jupyter Notebooks

Goals

  • Understand that, in an academic context, it is important to be able to read, use and re-use code.
  • Modular, comprehensible, re-usable and testable all come together.
  • Solving problems with the computer can be fun; working together on solving these problems doubles the fun.

Content

Preliminary content of the bootcamps can be as follows:

Day 1

Intro, short history of Python, characteristics of Python Jupyter Notebooks

Python basics I

  • simple expressions
  • assignment statement
  • conditional expressions
  • sets
  • lists
  • tuples
  • and other things to iterate over
  • dictionaries, comprehensions
  • defining one-line procedures

Python basics II

  • Using existing modules
  • Creating your own modules
  • Loops and conditional statements
  • Grouping in Python using indentation
  • Breaking out of a loop
  • Reading from a file

Datalab I: number guessing games (random, keeping track of scores, etc.)

Datalab II: cookbook.py (wordfrequency in Jane Austen's novel "Emma")

Python more in depth: More on numbers & text

Datalab III:

Aggregating data: Scraping, etc.

Day 2

CSV files, Pandas, data cleaning: tidy-data Text analysis with NLTK and/or textBlob, data cleaning

SQL BigQuery, visualizations

Day 3

Morning: Wrap-up, evaluation, etc.

Extras

  • Pointers to online resources