# python_workshops

**Repository Path**: flashriver/python_workshops

## Basic Information

- **Project Name**: python_workshops
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-17
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Python for Humanities and Social Sciences

This repository contains Jupyter notebooks for the series of workshops given by the [Center for Interdisciplinary Digital Research (CIDR)](http://library.stanford.edu/department/cidr), a unit of [Stanford University Libraries](http://library.stanford.edu/), and its associated partners, on Python related topics specially crafted towards the Humanities and Social Sciences.

## Introduction to Python [[Notebook](intro_to_python.ipynb) | [Solutions](intro_to_python_filled.ipynb)]

This workshop covers basic Python syntax and project set up through the teaching of basic web scraping with [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/).

## Data Manipulation and Visualization with Python [[Notebook](data_manipulation.ipynb) | [Solutions](data_manipulation_filled.ipynb)]

This workshop guides students through fundamentals of data manipulation and visualization with [Pandas](http://pandas.pydata.org/), [matplotlib](https://matplotlib.org/), and [Seaborn](http://seaborn.pydata.org/).

## Natural Language Processing with Python [[Notebook](intro_to_nlp.ipynb) | [Solutions](intro_to_nlp_filled.ipynb)]

This workshop teaches students natural language processing in Python, with topics such as tokenization, part of speech tagging, and sentiment analysis, using [TextBlob](https://textblob.readthedocs.io/en/dev/).

## Introduction to Machine Learning [[Notebook](intro_to_ml.ipynb) | [Solutions](intro_to_ml_filled.ipynb)]

This workshop introduces the basic workflow of machine learning in Python using [scikit-learn](http://scikit-learn.org/stable/). It covers topics from feature engineering or feature learning to model evaluation and selection.