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Introductory Python Series for UC Berkeley's D-Lab
Jupyter Notebook Python
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README.md

D-Lab Python Fundamentals Series

To sign up for the Python series and other workshops, please visit the D-Lab's website.

To run these lessons in the cloud, click this button: Binder

To run these lessons on your laptop, clone the repo on your laptop command line:

git clone https://github.com/dlab-berkeley/python-fundamentals.git

The D-Lab introductory series consists of four parts:

Part 1:

  • Running Python
  • Jupyter notebooks
  • Variables assignment
  • Types conversion
  • Strings
  • Built-ins

Part 2:

  • Lists
  • Loops
  • Conditionals
  • Functions
  • Scope

Part 3:

  • Dictionaries
  • Files
  • Libraries
  • Errors
  • Comprehensions

Part 4:

  • Python in Application

Parts 1-3 focus on learning the basics of programming in Python. This includes variables, data types, conditionals, functions, scope, debugging, and style. All of the materials aim to use examples from the social sciences and humanities in order to better relate to our target audience. In support of this, Part 4 is a day of application. Learners will work with a real-world text data set of UN documents, extracting targeted information and generating tabular data, ultimately writing to a .csv file suitable for subsequent statistical analysis. Everything needed to complete Part 4 is covered in parts 1-3.

Credits:

Much of these materials were adapted from those produced by Software Carpentry. Thank you!

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