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
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)
Python Track
-
Google's Python Class
-
Google Python Style Guide
- [Docs] (http://www.scipy.org/)
-
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