Learning Path: R Programming for Data Analysts

HDRips | MP4/AVC, ~1500 kb/s | 960x528 | Duration: 15:12:01 | English: AAC, 128 kb/s (2 ch) | 5,46 GB

Genre: Development / Programming

R Programming Data Analyst Learning Path, is a tour through the most important parts of R, the statistical programming language, from the very basics to complex modeling. It covers reading data, programming basics, visualization, data munging, regression, classification, clustering, modern machine learning, network analysis, web graphics, and techniques for dealing with large data, both in memory and in databases.

This 15-hour video teaches you how to program in R even if you are unfamiliar with statistical techniques. It starts with the basics of using R and progresses into data manipulation and model building. Users learn through hands-on practice with the code and techniques. New material covers chaining commands, faster data manipulation, new ways to read rectangular data into R, testing code, and the hot package Shiny.

Based on a course on R and Big Data taught by the author at Columbia

Designed from the ground up to help viewers quickly overcome R’s learning curve

Packed with hands-on practice opportunities and realistic, downloadable code examples

Presented by an author with unsurpassed experience teaching statistical programming and modeling to novices

For every potential R user: programmers, data scientists, DBAs, marketers, quants, scientists, policymakers, and many others

What You Will Learn:

Installing R

Basic math

Working with variables and different data types

Matrix algebra

data.frames

Reading data

Data aggregation and manipulation

plyr

dplyr

Making statistical graphs

Manipulate text

Automatically generate reports and slideshows

Display data with popular JavaScript libraries

Build Shiny dashboards

Build R packages

Incorporate C++ for faster code

Basic statistics

Linear models

Generalized linear models

Model validation

Decision trees

Random forests

Bootstrap

Time series analysis

Clustering

Network analysis

Automatic parameter tuning

Bayesian regression using Stan