R Programming: Advanced Analytics In R For Data Science
- Description
- Curriculum
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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:
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How to prepare data for analysis in R
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How to perform the median imputation method in R
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How to work with date-times in R
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What Lists are and how to use them
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What the Apply family of functions is
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How to use apply(), lapply() and sapply() instead of loops
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How to nest your own functions within apply-type functions
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How to nest apply(), lapply() and sapply() functions within each other
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And much, much more!
The more you learn, the better you will get. After every module, you will have a robust set of skills to take with you into your Data Science career.
We prepared real-life case studies.
In the first section, you will be working with financial data, cleaning it up, and preparing for analysis. You were asked to create charts showing revenue, expenses, and profit for various industries.
In the second section, you will be helping Coal Terminal understand what machines are underutilized by preparing various data analysis tasks.
In the third section, you are heading to the meteorology bureau. They want to understand better weather patterns and requested your assistance on that.
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Welcome Challenge! -
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Welcome to the Advanced R Programming Course! -
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Learning Paths
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Extra: Interview with Hadley Wickham
Hey there!
Since you're taking this Advanced Analytics in R course, I'm guessing you're quite comfortable with R and most likely familiar with the name Hadley Wickham (creator of GGPlot2 and countless other R packages).
Hadley is awesome! Is he not?
Well, then...
I've got a special surprise for you.
Earlier in 2020 Hadley joined me on the SuperDataScience podcast for a deep chat about R Programming, and today I'd like to share this interview with you:
https://www.superdatascience.com/podcast/hadley-wickham-talks-integration-and-future-of-python-and-r
Listen to it during your commute to work, a hike, or simply in the comfort of your home. You can find it via the link or by searching for the SuperDataScience Podcast on any podcast app or even Spotify and navigating to episode number #337.
In this episode you will learn:
Hadley’s R packages [8:26]
Better integrations between R and Python [20:11]
LinkedIn Q&A [33:34]
useR Conference vs. RStudio Conference [50:46]
LinkedIn Q&A: Career-related questions [1:01:06]
LinkedIn Q&A: Future-related questions [1:08:01]
I'm sure you're going to enjoy this inspiring interview!
- Kirill
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Get the materials
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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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Data Preparation
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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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R programming: What is a List?
In this lecture, you will know what the lists in R programming language are and how to create them
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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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Lists in R
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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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R programming: What is the Apply family?
Here you will know about 3 main functions from Apply family and will know how to use them
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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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"Apply" Family of Functions
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THANK YOU Video
