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R Programming: Advanced Analytics In R For Data Science

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R Programming: Advanced Analytics In R For Data Science. Take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2

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Descripción

The name of this course is R Programming: Advanced Analytics In R For Data Science. The knowledge you will get with this indescribable online course is astonishing. Take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2.
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Kirill Eremenko, SuperDataScience Team, one of the very best experts in this field.

Description of this course: R Programming: Advanced Analytics In R For Data Science

Course 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 Video training, 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 RHow to perform the median imputation method in RHow to work with date-times in RWhat Lists are and how to use themWhat the Apply family of functions isHow to use apply(), lapply() and sapply() instead of loopsHow to nest your own functions within apply-type functionsHow to nest apply(), lapply() and sapply() functions within each otherAnd 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.

Requirements of this course: R Programming: Advanced Analytics In R For Data Science

What are the requirements? Basic knowledge of R Knowledge of the GGPlot2 package is recommended Knowledge of dataframes Knowledge of vectors and vectorized operations

What will you learn in this course: R Programming: Advanced Analytics In R For Data Science?

What am I going to get from this course? 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 plot 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

Target audience of this course: R Programming: Advanced Analytics In R For Data Science

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

Información adicional

Instructor

Kirill Eremenko, SuperDataScience Team

Lectures

47

Length

6

Skill Level

Intermediate Level

Languages

English, captions

Includes

Lifetime access <br/> 30 day money back guarantee! <br/> Available on iOS and Android <br/> Certificate of Completion

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