Udemy - R Programming Advanced Analytics In R For Data Science [Getnewcourses]

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.3 GB
  • Uploaded Byabia9220
  • Downloads105
  • Last checkedJan. 23rd '20
  • Date uploadedJan. 22nd '20
  • Seeders 5
  • Leechers4
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Infohash : 0C7F217F11D421936678751F7A55E031DC7389DC

Udemy - R Programming Advanced Analytics In R For Data Science



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What you'll 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

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Requirements
Basic knowledge of R
Knowledge of the GGPlot2 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!

Udemy courses free download

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

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Who this course is for:
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

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Files:

Udemy - R Programming Advanced Analytics In R For Data Science 1. Welcome To The Course
  • 1. Welcome to the Advanced R Programming Course!.mp4 (29.1 MB)
  • 1. Welcome to the Advanced R Programming Course!.vtt (8.0 KB)
  • 2. BONUS Learning Paths.html (2.4 KB)
  • 3. Some Additional Resources!!.html (0.6 KB)
  • ReadMe.txt (0.2 KB)
2. Data Preparation
  • 3. Updates on Udemy Reviews.mp4 (58.3 MB)
  • 17. Replacing Missing Data Median Imputation Method (Part 1).mp4 (49.0 MB)
  • 11. An Elegant Way To Locate Missing Data.mp4 (48.4 MB)
  • 9. Dealing with Missing Data.mp4 (42.6 MB)
  • 15. Reseting the dataframe index.mp4 (39.2 MB)
  • 8. gsub() and sub().mp4 (33.1 MB)
  • 21. Visualizing results.mp4 (31.9 MB)
  • 12. Data Filters which() for Non-Missing Data.mp4 (30.0 MB)
  • 5. What are Factors (Refresher).mp4 (29.2 MB)
  • 1. Welcome to this section. This is what you will learn!.mp4 (26.7 MB)
  • 14. Removing records with missing data.mp4 (26.3 MB)
  • 6. The Factor Variable Trap.mp4 (24.5 MB)
  • 16. Replacing Missing Data Factual Analysis Method.mp4 (24.0 MB)
  • 7. FVT Example.mp4 (22.5 MB)
  • 13. Data Filters is.na() for Missing Data.mp4 (21.5 MB)
  • 4. Import Data into R.mp4 (19.3 MB)
  • 19. Replacing Missing Data Median Imputation Method (Part 3).mp4 (19.0 MB)
  • 20. Replacing Missing Data Deriving Values Method.mp4 (18.4 MB)
  • 18. Replacing Missing Data Median Imputation Method (Part 2).mp4 (15.6 MB)
  • 10. What is an NA.mp4 (14.0 MB)
  • 22. Section Recap.mp4 (10.9 MB)
  • 2. Project Brief Financial Review.mp4 (6.8 MB)
  • 17. Replacing Missing Data Median Imputation Method (Part 1).vtt (18.0 KB)
  • 21. Visualizing results.vtt (15.0 KB)
  • 6. The Factor Variable Trap.vtt (13.9 KB)
  • 11. An Elegant Way To Locate Missing Data.vtt (13.8 KB)
  • 8. gsub() and sub().vtt (13.1 KB)
  • 9. Dealing with Missing Data.vtt (12.6 KB)
  • 12. Data Filters which() for Non-Missing Data.vtt (12.5 KB)
  • 5. What are Factors (Refresher).vtt (10.4 KB)
  • 16. Replacing Missing Data Factual Analysis Method.vtt (9.4 KB)
  • 7. FVT Example.vtt (9.3 KB)
  • 19. Replacing Missing Data Median Imputation Method (Part 3).vtt (8.6 KB)
  • 22. Section Recap.vtt (7.8 KB)
  • 10. What is an NA.vtt (7.6 KB)
  • 13. Data Filters is.na() for Missing Data.vtt (7.4 KB)
  • 4. Import Data into R.vtt (7.2 KB)
  • 15. Reseting the dataframe index.vtt (6.7 KB)
  • 14. Removing records with missing data.vtt (6.4 KB)
  • 18. Replacing Missing Data Median Imputation Method (Part 2).vtt (6.3 KB)
  • 20. Replacing Missing Data Deriving Values Method.vtt (5.9 KB)
  • 2. Project Brief Financial Review.vtt (4.1 KB)
  • 3. Updates on Udemy Reviews.vtt (3.9 KB)
  • 1. Welcome to this section. This is what you will learn!.vtt (3.7 KB)
  • 23. Data Preparation.html (0.1 KB)
3. Lists in R
  • 2. Project Brief Machine Utilization.mp4 (53.1 MB)
  • 4. Handling Date-Times in R.mp4 (38.6 MB)
  • 10. Creating A Timeseries Plot.mp4 (38.3 MB)
  • 5. R programming What is a List.mp4 (36.0 MB)
  • 8. Adding and deleting components.mp4 (32.5 MB)
  • 9. Subsetting a list.mp4 (24.3 MB)
  • 1. Welcome to this section. This is what you will learn!.mp4 (17.8 MB)
  • 7. Extracting components lists [] vs [[]] vs $.mp4 (16.7 MB)
  • 3. Import Data Into R.mp4 (15.4 MB)
  • 6. Naming components of a list.mp4 (11.7 MB)
  • 11. Section Recap.mp4 (6.6 MB)
  • 2. Project Brief Machine Utilization.vtt (25.0 KB)
  • 5. R programming What is a List.vtt (14.2 KB)
  • 4. Handling Date-Times in R.vtt (13.6 KB)
  • 8. Adding and deleting components.vtt (12.5 KB)
  • 10. Creating A Timeseries Plot.vtt (11.7 KB)
  • 9. Subsetting a list.vtt (10.9 KB)
  • 7. Extracting components lists [] vs [[]] vs $.vtt (9.0 KB)
  • 3. Import Data Into R.vtt (7.9 KB)
  • 6. Naming components of a list.vtt (6.0 KB)
  • 11. Section Recap.vtt (4.6 KB)
  • 1. Welcome to this section. This is what you will learn!.vtt (2.3 KB)
  • 12. Lists in R.html (0.1 KB)
4. Apply Family of Functions
  • 15. THANK YOU bonus video.mp4 (52.2 MB)
  • 7. Using lapply().mp4 (38.7 MB)
  • 10. Using sapply().mp4 (34.9 MB)
  • 12. which.max() and which.min() (advanced topic).mp4 (32.4 MB)
  • 3. Import Data into R.mp4 (28.1 MB)
  • 9. Adding your own functions.mp4 (28.0 MB)
  • 1. Welcome to this section. This is what you will learn!.mp4 (27.7 MB)
  • 5. Using apply().mp4 (25.7 MB)
  • 2. Project Brief Weather Patterns.mp4 (25.3 MB)
  • 11. Nesting apply() functions.mp4 (24.9 MB)
  • 8. Combining lapply() with [].mp4 (24.8 MB)
  • 6. Recreating the apply function with loops (advanced topic).mp4 (19.8 MB)
  • 4. R programming What is the Apply family.mp4 (17.2 MB)
  • 13. Section Recap.mp4 (9.8 MB)
  • 12. which.max() and which.min() (advanced topic).vtt (14.8 KB)
  • 10. Using sapply().vtt (14.6 KB)
  • 7. Using lapply().vtt (14.6 KB)
  • 3. Import Data into R.vtt (13.6 KB)
  • 2. Project Brief Weather Patterns.vtt (12.8 KB)
  • 9. Adding your own functions.vtt (12.3 KB)
  • 5. Using apply().vtt (11.7 KB)
  • 11. Nesting apply() functions.vtt (10.7 KB)
  • 4. R programming What is the Apply family.vtt (10.4 KB)
  • 6. Recreating the apply function with loops (advanced topic).vtt (10.2 KB)
  • 8. Combining lapply() with [].vtt (9.9 KB)
  • 13. Section Recap.vtt (7.1 KB)
  • 1. Welcome to this section. This is what you will learn!.vtt (3.5 KB)
  • 15. THANK YOU bonus video.vtt (2.1 KB)
  • 14. Apply Family of Functions.html (0.1 KB)
5. Bonus Lectures
  • 1. YOUR SPECIAL BONUS.html (3.2 KB)
  • Visit Getnewcourses.com.url (0.3 KB)

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