1. Big data toolbox

2. R for statistical computing
Free software for statistical computing, data manipulation and graphics
Comprehensive R Archive Network (CRAN) – binary distributions of the base system and contributed packages
GitHub: open source access to R packages that are not available on CRAN

3. Python Free computing software
SciPy is a collection of packages for mathematics, science and engineering
Pandas is a data analysis and modeling library
IPython used for sharing, visualization and parallel computing

4. Educational resources
Statistical Learning and Data Mining Workshop (Hastie and Tibshirani of Stanford)
Coursera: Python; Machine learning; R
Google Developer R videos on YouTube:
Master of Information and Data Science (MIDS), UC Berkeley

5. Cloud resources Microsoft Azure Amazon Elastic MapReduce (EMR)
Storage
Distributed cloud computing (Hadoop)
Server R
Machine learning
Amazon Elastic MapReduce (EMR)
Google Cloud Platform

6. Dimension reduction Singular Value Decomposition (SVD)
Used to represent data efficiently
Reduces number of attributes that are used in data analysis/mining
Removes unnecessary data that are linearly dependent
Principal Components Analysis (PCA)
Maps correlated variables into uncorrelated variables called principal components (PC)
First PC accounts for largest amount of variability in data
Second PC accounts for largest amount of remaining variability available, and so on
These components can then replace the individual variables that create the components for analysis