SGLearn@R Programming: Adv Analytics In R for Data Science
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SGLearn@R Programming: Adv 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
0.0 (0 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5 students enrolled
Last updated 7/2017
English
Price: $199.99
30-Day Money-Back Guarantee
This course includes
  • 6 hours on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
  • 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
Requirements
  • Basic knowledge of R
  • Knowledge of the GGPlot2 package is recommended
  • Knowledge of dataframes
  • Knowledge of vectors and vectorized operations
Description

Welcome to the SGLearn Series targeted at Singapore-based learners picking up new skillsets and competencies. This course is an adaptation of the same course by Kirill Eremenko and is specially produced in collaboration with Kirill for Singaporean learners.

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

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
Course content
Expand all 47 lectures 05:51:22
+ Data Preparation
21 lectures 02:26:04
Welcome to this section. This is what you will learn!
02:43
Import Data into R
05:10
What are Factors (Refresher)
07:37
FVT Example
06:34
gsub() and sub()
09:44
Dealing with Missing Data
09:32
What is an NA?
05:15
An Elegant Way To Locate Missing Data
10:01
Data Filters: which() for Non-Missing Data
08:57
Data Filters: is.na() for Missing Data
05:52
Removing records with missing data
04:47
Reseting the dataframe index
05:03
Replacing Missing Data: Factual Analysis Method
06:48
Replacing Missing Data: Median Imputation Method (Part 1)
13:09
Replacing Missing Data: Median Imputation Method (Part 2)
04:29
Replacing Missing Data: Median Imputation Method (Part 3)
06:14
Replacing Missing Data: Deriving Values Method
04:33
Visualizing results
10:49
Section Recap
05:49
Data Preparation
10 questions
+ Lists in R
11 lectures 01:28:47
Welcome to this section. This is what you will learn!
01:44
Project Brief: Machine Utilization
17:44
Import Data Into R
05:58
Handling Date-Times in R
10:17
Naming components of a list
04:35
Extracting components lists: [] vs [[]] vs $
06:46
Adding and deleting components
09:35
Subsetting a list
08:05
Creating A Timeseries Plot
09:12
Section Recap
03:32
Lists in R
5 questions
+ "Apply" Family of Functions
13 lectures 01:48:47
Welcome to this section. This is what you will learn!
02:40
Project Brief: Weather Patterns
08:49
Import Data into R
09:46
Using apply()
08:33
Recreating the apply function with loops (advanced topic)
07:39
Using lapply()
11:02
Combining lapply() with []
07:10
Adding your own functions
09:33
Using sapply()
10:58
Nesting apply() functions
08:19
which.max() and which.min() (advanced topic)
11:32
Section Recap
05:12
"Apply" Family of Functions
5 questions
+ Bonus Lectures
1 lecture 01:59
***YOUR SPECIAL BONUS***
01:59