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Software Development / Big Data & Analytics

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

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.
Full details

Curriculum

  • Welcome To The Course
    Welcome to the Advanced R Programming Course!
    5:45
  • Data Preparation
    Welcome to this section. This is what you will learn!
    2:44
    Project Brief: Financial Review
    2:50
    Import Data into R
    5:11
    What are Factors (Refresher)
    7:38
    The Factor Variable Trap
    10:10
    FVT Example
    6:35
    gsub() and sub()
    9:45
    Dealing with Missing Data
    9:33
    What is an NA?
    5:16
    An Elegant Way To Locate Missing Data
    10:02
    Data Filters: which() for Non-Missing Data
    8:58
    Data Filters: is.na() for Missing Data
    5:53
    Removing records with missing data
    4:48
    Reseting the dataframe index
    5:04
    Replacing Missing Data: Factual Analysis Method
    6:49
    Replacing Missing Data: Median Imputation Method (Part 1)
    13:10
    Replacing Missing Data: Median Imputation Method (Part 2)
    4:30
    Replacing Missing Data: Median Imputation Method (Part 3)
    6:15
    Replacing Missing Data: Deriving Values Method
    4:34
    Visualizing results
    10:50
    Section Recap
    5:50
  • Lists in R
    Welcome to this section. This is what you will learn!
    1:45
    Project Brief: Machine Utilization
    17:45
    Import Data Into R
    5:59
    Handling Date-Times in R
    10:18
    What is a List? 
    11:20
    Naming components of a list
    4:36
    Extracting components lists
    6:47
    Adding and deleting components
    9:36
    Subsetting a list
    8:06
    Creating A Timeseries Plot
    9:13
    Section Recap
    3:33
  • “Apply” Family Of Functions
    Welcome to this section. This is what you will learn!
    2:41
    Project Brief: Weather Patterns
    8:50
    Import Data into R
    9:47
    What is the Apply family?
    7:35
    Using apply()
    8:34
    Recreating the apply function with loops (advanced topic)
    7:40
    Using lapply()
    11:03
    Combining lapply with square brackets
    7:11
    Adding your own functions
    9:34
    Using sapply()
    10:59
    Nesting apply() functions
    8:20
    which.max() and which.min() (advanced topic)
    11:33
    Section Recap
    5:13

Skills

  • Data Analytics
  • Data Science
  • R Programming

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