Author: ziuziu on 18-11-2016, 13:40
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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, able 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




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