Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques

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Deep Learning Data-Science-45min-Intros
Overview

Data Science 45-min Intros

Every week*, our data science team @Gnip (aka @TwitterBoulder) gets together for about 50 minutes to learn something.

While these started as opportunities to collectively "raise the tide" on common stumbling blocks in data munging and analysis tasks, they have since grown to machine learning, statistics, and general programming topics. Anything that will help us do our jobs better is fair game.

For each session, someone puts together the lesson/walk-through and leads the discussion. Presentation platforms commonly include well-written READMEs, IPython notebooks, knitr documents, interactive code sessions... the more hands-on, the better.

Feel free to use these for your own (or your team's) growth, and do submit pull requests if you have something to add.

*ok, while we try to do it every week, sometimes it doesn't happen. In that case, we try to guilt trip the person who slacked.

Current topics

Python

Bash + command-line tools

Statistics

Machine Learning

Natural Langugage Processing

Network structure

Algorithms

Engineering

Geographic Information Systems

Web development

Visualization

Databases

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Comments
  • Remove outdated comments and revert changes made during RST.

    Remove outdated comments and revert changes made during RST.

    Before adding the "Foreword" on the Python RNG, there were some comments about inconsistencies in result data. These are removed in this commit.

    opened by jrmontag 0
  • Add decorators session

    Add decorators session

    Include a new subdirectory with an introduction to Python decorators. Note that there is a compiled file included (specified in local .gitignore) for demonstration purposes. This won't affect the larger gitignore of .pyc files.

    opened by jrmontag 0
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Director, Data Science
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