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

Ratings 4.62 / 5.00

R Programming: Advanced Analytics In R For Data Science

What You Will 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 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

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 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 have a robust set of skills to take with you into your Data Science career.

We prepared real-life case studies.

In the first section, you will be working with financial data, cleaning it up, and preparing for analysis. You were asked to create charts showing revenue, expenses, and profit for various industries.

In the second section, you will be helping Coal Terminal understand what machines are underutilized by preparing various data analysis tasks.

In the third section, you are heading to the meteorology bureau. They want to understand better weather patterns and requested your assistance on that.

Who Should Attend!

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

TAKE THIS COURSE