Repo of information for getting up to speed for Kaggle competition participation

• [Open Data Science Masters] (http://datasciencemasters.org/)

The dataset is provided by Kaggle ordinarily. If you are interested in practicing here is a great list of free data sources

• US Government's Open Data

The following models are common supervised learning model

• [Gradient boosting] (http://en.wikipedia.org/wiki/Gradient_boosting)
• [Random forest] (http://en.wikipedia.org/wiki/Random_forest)
• [Support vector machine] (http://en.wikipedia.org/wiki/Support_vector_machine)

• An Introduction to Statistical Learning: with Applications in R

• Data Science Centre

• Python For Data Analysis

Python Track

• Google's Python Class

  • Link
  • Videos
  • Problems
  • Material

• Google Python Style Guide

  • Style Guide

• Docs

• Docs

• Docs

• [Docs] (http://www.scipy.org/)

• Docs

• Docs
• Video Tutorial

• Learn Python Through Public Data Hacking

  • [Youtube Video](https: //www.youtube.com/watch?v=RrPZza_vZ3w)
  • Code Samples and Resources

• Twitter Sentiment Analysis with Python

  • Instructions
  • [GitHub Repo](git clone https://github.com/uwescience/datasci_course_materials.git)

RStudio, a GUI development platform.

The following libraries are frequently used in data analysis and modeling. Spend some time on studying them and they would eventually become your powerful weapons.

• plyr - manipulating data frame.
• caret - data modeling
• ggplot2 - graph plotting

• Tutorial for Kaggle titantic

• https://www.kaggle.com/c/titanic
• https://www.kaggle.com/c/bioresponse