“R Programming: Advanced Analytics In R For Data Science” will help the students to apply the factual analysis method to replace missing records. It will also help the students to understand how the applied family of functions works and explain why NA is a third type of logical constant. Professionals who are set to start a career in R programming language for data science can apply for this course.
This is a 6-hour long course, and it comprises real-life challenges which will make the applicant industry ready. Currently, udemy is offering an R Programming: Advanced Analytics In R For Data Science course for up to 87 % off i.e. INR 449 (INR 3,499).
Who can opt for this course?
- Anyone who wants to advance their skills in R and already has a foundational understanding of the language
- Everyone who has finished the R Programming A-Z course
- This course is not intended for R absolute beginners
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 05 Hours |
| Rating | 4.6/5 |
| Student Enrollment | 57,775 students |
| Instructor | Kirill Eremenko https://www.linkedin.com/in/kirilleremenko |
| Topics Covered | Extra: Interview with Hadley Wickham, What are Factors (Refresher), Removing records with missing data, Removing records with missing data |
| Course Level | Beginner |
| Total Student Reviews | 8,209 |
Learning Outcomes
- R data preparation is performed
- Find records that are absent in data frames
- Find any missing information in your data frames
- Replace any missing records by using the median imputation technique
- Replace any records that are missing by using the Factual Analysis approach
- Recognize how to employ the which() function
- the data frame index can be reset if you know how
- Replace strings by using the sub() and sub() methods
- Why is NA a third kind of logical constant? Relate to date and time in R
- Put date and time data in POSIXct time format
- In R, you may make, utilize, add to, edit, rename, access, and subset Lists
- When working with lists, be aware of when to use [] and when to use [[]] or the dollar symbol
- In R, make a time-series plot
- Recognize the functionality of the Apply family of functions
- Incorporate a for() loop to recreate an apply statement
- When working with matrices, use apply()
- When dealing with lists and vectors, use the apply() and apply() functions
- Apply statements can now include your custom functions
- Nest the functions apply(), apply(), and apply() inside one another
- Use the functions which
- max() and which min()
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Welcome To The Course (09 minutes) | Welcome to the Advanced R Programming Course! |
| Learning Paths | ||
| Extra: Interview with Hadley Wickham | ||
| Get the materials | ||
| Your Shortcut To Becoming A Better Data Scientist! | ||
| Study Tips For Success | ||
| 2. | Data Preparation (02 hours 26 minutes) | Welcome to this section. This is what you will learn! |
| Project Brief: Financial Review | ||
| Import Data into R | ||
| What are Factors (Refresher) | ||
| The Factor Variable Trap | ||
| FVT Example | ||
| gsub() and sub() | ||
| Dealing with Missing Data | ||
| What is an NA? | ||
| An Elegant Way To Locate Missing Data | ||
| Data Filters: which() for Non-Missing Data | ||
| Data Filters: is.na() for Missing Data | ||
| Removing records with missing data | ||
| Resetting the data frame index | ||
| Replacing Missing Data: Factual Analysis Method | ||
| Replacing Missing Data: Median Imputation Method (Part 1) | ||
| Replacing Missing Data: Median Imputation Method (Part 2) | ||
| Replacing Missing Data: Median Imputation Method (Part 3) | ||
| Replacing Missing Data: Deriving Values Method | ||
| Visualizing results | ||
| Section Recap | ||
| Data Preparation | ||
| 3. | Lists in R (01 hour 28 minutes) | Welcome to this section. This is what you will learn! |
| Project Brief: Machine Utilization | ||
| Import Data Into R | ||
| Handling Date-Times in R | ||
| R programming: What is a List? | ||
| Naming components of a list | ||
| Extracting components lists: [] vs [[]] vs $ | ||
| Adding and deleting components | ||
| Subsetting a list | ||
| Creating A Timeseries Plot | ||
| Section Recap | ||
| Lists in R | ||
| 4. | “Apply” Family of Functions (01 hour 51 minutes) | Welcome to this section. This is what you will learn! |
| Project Brief: Weather Patterns | ||
| Import Data into R | ||
| R programming: What is the Apply family? | ||
| Using apply() | ||
| Recreating the apply function with loops (advanced topic) | ||
| Using apply() | ||
| Combining apply() with [] | ||
| Adding your own functions | ||
| Using apply() | ||
| Nesting apply() functions | ||
| which.max() and which.min() (advanced topic) | ||
| Section Recap | ||
| “Apply” Family of Functions | ||
| THANK YOU Video | ||
| 5. | Special Offer (01 minutes) | ***YOUR SPECIAL BONUS*** |
Resources Required
- Basic understanding of R
- The GGPlot2 package should be familiar to you
- Understanding of data frames
- Understanding of vectors and operations that use them
Featured Review
Bhanurajaraju P (5/5): Great to be a part of the course! This course has provided ample scope to learn techniques and methods to horn Data Analysis Skills …Thanks Kirill
Pros
- Subramaniam B (5/5): I am highly impressed by Kirrill’s knowledge and enthusiasm which is rubbing on me.
- Athanasios Galiopoulos (5/5): Kirill is one of the best instructors I’ve ever had, he explains the material in a way that I understand and can follow along with and has a way to keep me engaged through the course!
- Francois Strijdom (4/5): Nevertheless, excellent course, and will continue to work through courses created by Kirill.
- Rajendra Choure (5/5): This course is the best choice made for me by my son.
Cons
- Rudolf Nyitray (1/5): It could have been included in the R Basics course, and no one would have complained about it.
- Marc Stamp (2/5): The code setup feels very simplistic for an Advanced R course, as well as there is a frustrating amount of code repetition and no best practices for when applying the initial EDA and data cleaning.
- Marc Mar (2/5): It’s shorter, doesn’t provide any homework and is not advanced.
About the Author
The instructor of this course is Kirill Eremenko who is a Data Scientist, with a 4.5 instructor rating and 599,095 reviews on Udemy. He offers 59 courses and has taught 2,251,523 students so far.
- Professionally, Kirill Eremenko is a data science consultant with experience in the retail, transportation, retail, and financial sectors
- At Deloitte Australia, Kirill Eremenko has received training from the top analytics mentors, and since he started teaching on Udemy, he has shared his experience with thousands of aspiring data scientists.
- Students will see from his courses how he gave skilled step-by-step tutoring in the field of data science by fusing my real-world expertise and academic background in physics and mathematics.
- His emphasis on intuitive explanations is one of his teaching strengths, so the students can be confident that they will fully comprehend even the most challenging subjects.
- To sum up, he is entirely and completely excited about data science.
Comparison Table
| Parameters | R Programming: Advanced Analytics In R For Data Science | Data Science and Machine Learning Bootcamp with R | Statistics for Business Analytics and Data Science A-Z™ |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 6 hours | 18 hours | 6 hours |
| Rating | 4.6 /5 | 4.7 /5 | 4.5 /5 |
| Student Enrollments | 57,771 | 86,125 | 57,350 |
| Instructors | Kirill Eremenko | Jose Portilla | Kirill Eremenko |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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