R Programming: Advanced Analytics In R For Data Science

Author: sddd on 23-09-2017, 05:21
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R Programming: Advanced Analytics In R For Data Science
R Programming: Advanced Analytics In R For Data Science
$200 | Duration: 6 hours | Video: h264, 1920x1080 | Audio: AAC, 44100 Hz, 2 Ch | 1.4 GB
Genre: eLearning | Language: English | Project Files


R Programming: Advanced Analytics In R For Data Science

What Will I Learn?
Perform Data Preparation in R
Identify missing records in dataframes
Locate missing data in your dataframes
Apply the Median Imputation method to replace missing records
Apply the Factual Analysis method to replace missing records
Understand how to use the which() function
Know how to reset the dataframe index
Work with the gsub() and sub() functions for replacing strings
Explain why NA is a third type of logical constant
Deal with date-times in R
Convert date-times into POSIXct time format
Create, use, append, modify, rename, access and subset Lists in R
Understand when to use and when to use or the $ sign when working with Lists
Create a timeseries Description in R
Understand how the Apply family of functions works
Recreate an apply statement with a for() loop
Use apply() when working with matrices
Use lapply() and sapply() when working with lists and vectors
Add your own functions into apply statements
Nest apply(), lapply() and sapply() functions within each other
Use the which.max() and which.min() functions
Requirements
Basic knowledge of R
Knowledge of the GGDescription2 package is recommended
Knowledge of dataframes
Knowledge of vectors and vectorized operations
Description
Ready to take your R Programming skills to the next level?
Want to truly become proficient at Data Science and Analytics with R?
This course is for you!
Professional R , unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the REAL WORLD.
In this course you will learn:
How to prepare data for analysis in R
How to perform the median imputation method in R
How to work with date-times in R
What Lists are and how to use them
What the Apply family of functions is
How to use apply(), lapply() and sapply() instead of loops
How to nest your own functions within apply-type functions
How to nest apply(), lapply() and sapply() functions within each other
And much, much more!
The more you learn the better you will get. After every module you will already have a strong set of skills to take with you into your Data Science career.
Who is the target audience?
Anybody who has basic R knowledge and would like to take their skills to the next level
Anybody who has already completed the R Programming A-Z course
This course is NOT for complete beginners in R
Download link :
(If you need these, buy and download immediately before they are delete)

Links are Interchangeable - Single Extraction - Premium is support resumable
R Programming: Advanced Analytics In R For Data Science
R Programming: Advanced Analytics In R For Data Science
$200 | Duration: 6 hours | Video: h264, 1920x1080 | Audio: AAC, 44100 Hz, 2 Ch | 1.4 GB
Genre: eLearning | Language: English | Project Files


R Programming: Advanced Analytics In R For Data Science

What Will I Learn?
Perform Data Preparation in R
Identify missing records in dataframes
Locate missing data in your dataframes
Apply the Median Imputation method to replace missing records
Apply the Factual Analysis method to replace missing records
Understand how to use the which() function
Know how to reset the dataframe index
Work with the gsub() and sub() functions for replacing strings
Explain why NA is a third type of logical constant
Deal with date-times in R
Convert date-times into POSIXct time format
Create, use, append, modify, rename, access and subset Lists in R
Understand when to use and when to use or the $ sign when working with Lists
Create a timeseries Description in R
Understand how the Apply family of functions works
Recreate an apply statement with a for() loop
Use apply() when working with matrices
Use lapply() and sapply() when working with lists and vectors
Add your own functions into apply statements
Nest apply(), lapply() and sapply() functions within each other
Use the which.max() and which.min() functions
Requirements
Basic knowledge of R
Knowledge of the GGDescription2 package is recommended
Knowledge of dataframes
Knowledge of vectors and vectorized operations
Description
Ready to take your R Programming skills to the next level?
Want to truly become proficient at Data Science and Analytics with R?
This course is for you!
Professional R , unique datasets designed with years of industry experience in mind, engaging exercises that are both fun and also give you a taste for Analytics of the REAL WORLD.
In this course you will learn:
How to prepare data for analysis in R
How to perform the median imputation method in R
How to work with date-times in R
What Lists are and how to use them
What the Apply family of functions is
How to use apply(), lapply() and sapply() instead of loops
How to nest your own functions within apply-type functions
How to nest apply(), lapply() and sapply() functions within each other
And much, much more!
The more you learn the better you will get. After every module you will already have a strong set of skills to take with you into your Data Science career.
Who is the target audience?
Anybody who has basic R knowledge and would like to take their skills to the next level
Anybody who has already completed the R Programming A-Z course
This course is NOT for complete beginners in R
Download link :
(If you need these, buy and download immediately before they are delete)
Links are Interchangeable - Single Extraction - Premium is support resumable
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