R Programming: Advanced Analytics In R For Data Science
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
Course content
- Preview05:44
- 00:34BONUS: Learning Paths
- 00:39BONUS: Interview with Hadley Wickham
- 00:05Get the materials
- 02:05Your Shortcut To Becoming A Better Data Scientist!
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
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Instructors
- 4.5 Instructor Rating
- 465,042 Reviews
- 1,578,453 Students
- 119 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!
- 4.5 Instructor Rating
- 428,470 Reviews
- 1,492,927 Students
- 105 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