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Material that I use for a variety of classes and tutorials
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01-Introduction_to_Python Moved Intro to Python to separate repositoru Dec 2, 2018
02-SQL Moved database/SQL material to separate repository Mar 14, 2019
03-Pandas Revised notes Jun 24, 2019
04-WebAPIs Revised notes Jun 24, 2019
05-Regular_Expressions Add files via upload Feb 12, 2019
06-Web_Scraping Functions Oct 12, 2018
07-TextMining_NLP New Notes Oct 3, 2018
08-Visualization Updated Census API May 22, 2019
09-Flask HTML forms discussion Nov 7, 2018
10-Slack Moved files out of Beta Feb 12, 2018
11-Using_R_in_Jupyter Moving directories around Dec 7, 2017
12-UNIX_Basics Revised Regex and Crawling notes Oct 10, 2018
13-Network_Analysis Create README.md May 6, 2019
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jupyterhub Add JupyterHub single-user files Dec 22, 2017
.gitattributes Added CSV files in LSF Nov 11, 2017
.gitignore Modified string similarity notes May 19, 2017
COURSES.md Create COURSES.md Mar 28, 2018
DATA_SOURCES.md Added pointers to APIs May 24, 2019
LICENSE Update LICENSE May 1, 2018
README.md Fix typos. Jul 4, 2018
start_jupyter.sh New version of Python notes Sep 5, 2017
stop_jupyter.sh Modified start-up scripts. Added Spacy Jan 16, 2017
sync_data.sh Small changes to the notes Feb 5, 2018
sync_notebooks.sh New sync mechanism Sep 22, 2017
test_notebooks.ipynb Moved Intro to Python to separate repositoru Dec 2, 2018
upgrade_linux.sh Adding scripts Jan 11, 2017
upgrade_python.sh Adding scripts Jan 11, 2017

README.md

This repository contains notes for various classes and seminars that I teach at NYU. They are focused on teaching programming for data science to non-CS majors. The emphasis is on offering live examples that students can use directly to complete their goals.

Accessing your Data Science Environment

We setup and deploy our data science environment (effectively, Jupyter with Python and R support, plus MySQL) using docker. As our default option, we allow students to connect to a JupyterHub server that runs on Kubernetes. We also give the option to students to run the same environment locally on their laptops, or deploy the Docker image on AWS or Google Cloud.

Data Sets

Related Courses

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