Statistical Software (2014 Spring)

Introduction

This course is designed as the advanced level course of Statistical Computing (under graduate level). You will learn various topics like statistical parallel computing, techniques for big data analysis,  text basic mining tools, statistical vitalization tools, developing your own packages.

Prerequisites

• Basic knowledge of statistical programming
• Basic knowledge of R

Course Literature

• Writing R extensions

Course contents

Part I: Efficient statistical programming

• Introduction
• Loops and vectorization
• Parallel Concepts
• Debugging
• Optimization | Matrix Derivatives
• R Graphics

Part II: Statistical programming for big data

• Big Data with Linear Models
• Big Data with bigmemory
• Logistic Regression with GPU
• Big Data Visualization Tools | R code

Part III: Special topics

• Distribute your statistical software | Example R package
• Bayesian Computations

Lab Exercises

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