R Programming: Advanced Analytics In R For Data Science
This is a subtitle. Get more detailed about your course here!
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
Course Curriculum
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1
Welcome To The Course
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Welcome to the Advanced R Programming Course!
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2
Data Preparation
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Welcome to this section. This is what you will learn!
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Project Brief: Financial Review
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Import Data into R
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What are Factors (Refresher)
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The Factor Variable Trap
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FVT Example
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gsub() and sub()
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Dealing with Missing Data
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What is an NA?
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An Elegant Way To Locate Missing Data
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Data Filters: which() for Non-Missing Data
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Data Filters: is.na() for Missing Data
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Removing records with missing data
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Reseting the dataframe index
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Replacing Missing Data: Factual Analysis Method
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Replacing Missing Data: Median Imputation Method (Part 1)
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Replacing Missing Data: Median Imputation Method (Part 2)
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Replacing Missing Data: Median Imputation Method (Part 3)
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Replacing Missing Data: Deriving Values Method
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Visualizing results
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Section Recap
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Quiz 1 – Data Preparation
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3
Lists in R
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Welcome to this section. This is what you will learn!
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Project Brief: Machine Utilization
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Import Data Into R
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Handling Date-Times in R
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What is a List?
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Naming components of a list
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Extracting components lists: [] vs [[]] vs $
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Adding and deleting components
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Subsetting a list
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Creating A Timeseries Plot
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Section Recap
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Quiz 2 – Lists in R
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4
“Apply” Family Of Functions
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Welcome to this section. This is what you will learn!
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Project Brief: Weather Patterns
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Import Data into R
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What is the Apply family?
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Using apply()
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Recreating the apply function with loops (advanced topic)
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Using lapply()
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Combining lapply() with []
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Adding your own functions
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Using sapply()
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Nesting apply() functions
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which.max() and which.min() (advanced topic)
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Section Recap
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Quiz 3 – “Apply” Family of Functions
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About the instructor
IT Manager
Eric Chu
A proven and experienced IT Manager, customer-focused and equipped with a technical background. I have over 8 years’ experience in IT Service Delivery Management and leading small to medium crossfunctional teams, and over 15 years of experience in IT Training and Sales.Through my passion for technology, science, and innovation, I have fostered extensive practical knowledge of emerging technologies, complex networks, and data centre eco-systems.I have had the opportunity to work across the Asia Pacific region as an experienced Business Development Manager and IT Project Manager delivering end to end solutions.