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30-Day Money-Back Guarantee

This course includes:

  • 6 hours on-demand video
  • 5 articles
  • Full lifetime access
  • Access on mobile and TV
Development Data Science R

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
Highest Rated
Rating: 4.7 out of 54.7 (6,588 ratings)
48,091 students
Created by Kirill Eremenko, SuperDataScience Team
Last updated 1/2021
English
English, French [Auto], 
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

5 sections • 53 lectures • 5h 58m total length

  • Preview05:44
  • BONUS: Learning Paths
    00:34
  • BONUS: Interview with Hadley Wickham
    00:39
  • Get the materials
    00:05
  • Your Shortcut To Becoming A Better Data Scientist!
    02:05

  • Welcome to this section. This is what you will learn!
    02:43
  • Preview02:49
  • Updates on Udemy Reviews
    01:09
  • Import Data into R
    05:10
  • What are Factors (Refresher)
    07:37
  • Preview10:09
  • 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

  • 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
  • R programming: What is a List?
    Preview11:19
  • 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

  • Welcome to this section. This is what you will learn!
    02:40
  • Project Brief: Weather Patterns
    08:49
  • Import Data into R
    09:46
  • R programming: What is the Apply family?
    Preview07:34
  • 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
  • THANK YOU bonus video
    02:40

  • ***YOUR SPECIAL BONUS***
    01:53

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!

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

Featured review

Supriti Swati
Supriti Swati
21 courses
7 reviews
Rating: 5.0 out of 5a year ago
It was an extremely joyful experience doing this course. Thes best part is I got to work with actual data. Thanks Kirill. Best course to do if anyone has already done the beginner and moderate level (wrapped in one) course by Kirill only. I took the preview of so many courses in Linked Learning and Udemy also. This is amazing. Thanks Alex for helping me in all possible way throughout the course.

Instructors

Kirill Eremenko
Data Scientist
Kirill Eremenko
  • 4.5 Instructor Rating
  • 455,856 Reviews
  • 1,738,371 Students
  • 50 Courses

My name is Kirill Eremenko and I am super-psyched that you are reading this!

Professionally, I am a Data Science management consultant with over five years of experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and today I leverage Big Data to drive business strategy, revamp customer experience and revolutionize existing operational processes.

From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. I am also passionate about public speaking, and regularly present on Big Data at leading Australian universities and industry events.

To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!

SuperDataScience Team
Helping Data Scientists Succeed
SuperDataScience Team
  • 4.5 Instructor Rating
  • 457,480 Reviews
  • 1,648,957 Students
  • 107 Courses

Hi there,

We are the SuperDataScience Social team. You will be hearing from us when new SDS courses are released, when we publish new podcasts, blogs, share cheatsheets and more!

We are here to help you stay on the cutting edge of Data Science and Technology. 

See you in class,

Sincerely,

The Real People at SuperDataScience

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