R Programming Hands-on Specialization For Data Science (lv1)

Category: Business

Posted on 2019-03-29, by fubber.



R Programming Hands-on Specialization For Data Science (lv1)
mp4 | AVC: 1280x720 G_P| Audio:AAC LC 192 Kbps | Total files:191 | mp4, txt, pdf, vtt, html | 1.34 GB
Genre: eLearning | Language: English


What you'll learn

Setup and Use Development Environment for R
Install and Use Packages in R
Learn and use Atomic Data Types in R
Learn and apply advanced explicit/Implicit Coercioning in R
Learn multiple approaches to create vectors in R
Understand nuances and implications in Vector Coercions
Understand Vector indexing principles in R
Understand and leverage Vectors' flatness property
Understand Vector Labels and Attributes and their practical use-cases
Learn Matrices and multiple approaches for creation
Learn how Matrices Dimension Property works
Learn advanced techniques for Matrices Indexing
Learn Matrices Operations and Important Functions
Learn the amazing use-cases of Lists
Learn to leverage Lists' Recursive Nature
Learn multiple ways to create Lists (including from other data structures)
Learn critical nuances in Lists Indexing, Labels and Lists Properties
Learn multiple approaches to create Data Frames (including from other data structures)
Learn Data Frames sub-setting (beginner to advanced)
Learn how to impute missing values in Data Frames for efficient Data Analysis
Learn R Control Structures (Conditional statements and loops)
Learn to create and use R Functions
Understand Web Scraping Process
Learn R's Apply family of functions for advanced data manipulation
Learn Multiple ways to perform Web Scraping in R
Learn how to perform Data Munging, Cleansing and Transformation in R
Learn HTML and Document Object Model in the context of Web Scraping
Learn XPath Query Language for Web Scraping
Learn RSelenium setup and usage for advanced Web Scraping
Learn Regular Expression Functions in R for advanced analysis
Learn advanced Data Frames techniques for efficient data analysis
Learn how to perform statistical analysis and visualisation to derive insights in R


Width: 1280 pixels
Height: 720 pixels
Format: AVC
Codec: AVC
Duration: 3min 21s
Bit rate: 29 Kbps
Frame rate: 30.000 fps
Aspect ratio: 16:9
Bit depth: 8 bits
Color space: YUV
Subtitles: N/A


Audio track: 1
Language: N/A
Codec: AAC LC
Channels: 2
Bit rate: 192 Kbps N/A
Compression: Lossy
Sampling rate: 48 Khz


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