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    R Programming: Advanced Analytics In R For Data Science

    BY
    Udemy

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    Mode

    Online

    Fees

    ₹ 4099

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 4,099
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Welcome To The Course

    • Welcome to the Advanced R Programming Course!
    • BONUS: Learning Paths
    • BONUS: Interview with Hadley Wickham
    • Get the materials
    • Your Shortcut To Becoming A Better Data Scientist!

    Data Preparation

    • Welcome to this section. This is what you will learn!
    • Project Brief: Financial Review
    • Updates on Udemy Reviews
    • 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
    • Reseting the dataframe 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

    Lists in R

    • 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

    "Apply" Family of Functions

    • 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 lapply()
    • Combining lapply() with []
    • Adding your own functions
    • Using sapply()
    • Nesting apply() functions
    • which.max() and which.min() (advanced topic)
    • Section Recap
    • "Apply" Family of Functions
    • THANK YOU bonus video

    Bonus Lectures

    • ***YOUR SPECIAL BONUS***

    Instructors

    Mr Kirill Eremenko

    Mr Kirill Eremenko
    Data Scientist
    Udemy

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