Do you want to truly become proficient at Data Science and Analytics with R? If yes, then this course is for you that will take your R Programming skills to the next level. This online courses is created by Kirill Eremenko, SuperDataScience Team. It has 4.6 ratings out of 5 and more than 6,864 students has already enrolled.
This course by Kirill Eremenko will provide you professional R video training, unique data sets 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. It teaches you how to perform Data Preparation in R and identify missing records in data frames. You will get to know how to locate missing data in your data frames and apply the Median Imputation method to replace missing records.
This course let you know how to apply the factual analysis method to replace missing records. There are some important things you will get to know in this course such as to understand how to use the which() function, how to reset the data frame index, and to work with the gsub() and sub() functions for replacing strings. Moreover, It will explain why NA is a third type of logical constant and deal with date-times in R.
Following are some points you will learn in this course:
- How to convert date-times into POSIXct time format and create, use, append, modify, rename, access and subset Lists in R
- How to understand when to use  and when to use [] or the $ sign when working with Lists and create a timeseries plot in R
- How to apply family of functions works and recreate an apply statement with a for() loop
- How to use apply() when working with matrices and use lapply() and sapply() when working with lists and vectors
- How to add your own functions into apply statements and nest apply(), lapply() and sapply() functions within each other
- How to use the which.max() and which.min() functions
The more you learn the better you will get to know and after every module you will already have a strong set of skills to take with you into your Data Science career.
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