About This Course

To be a well-rounded data scientist, it is imperative to know the ins and outs of both Python and R, the latter of which we will discuss in this course. In addition to all the primary coding functionality, we will learn more about what makes R unique, diving into how R leverages vectors and matrices to read in and work with data. 

Additionally, we will discuss the mechanics of the powerful dplyr library, which allows extreme ease in manipulating datasets. We will then discuss data visualization, highlighting the advantages of the highly customizable ggplot2 library, which powers some of the most intricate graphs seen in data journalism and research today.

Finally, we will go through basic statistics and discuss the statistical tools within R, such as constructing confidence intervals, hypothesis testing, and ANOVA testing.

We will cover the following topics:

  • R fundamentals
  • Working with data: Dplyr
  • Data visualisation in R
  • Statistics

Who is this course suitable for?

Anyone who is looking to understand more about R Programming with a view to break into a career in either software development, Data Analysis or Data Science. Equally, you may be a professional working in tech or finance and want to learn new analytical techniques that can enhance your overall skillset.

Our Promise to You

By the end of this course, you will have learned R programming.

30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about the fundamentals of R programming.

Course Curriculum

Section 1 - R Fundamentals
RStudio And RMarkdown 00:00:00
R vs. Python, Basic Math, Boolean 00:00:00
Variable Assignment 00:00:00
Vectors 00:00:00
Matrices 00:00:00
Some Useful Vector-Based Functions 00:00:00
Strings And Characters 00:00:00
Conditionals 00:00:00
For Loops And While Loops 00:00:00
Defining Functions 00:00:00
Shiny Dashboards 00:00:00
Section 2 - Working With Data:Dplyr
Intro To Working With Data 00:00:00
Select 00:00:00
Filter 00:00:00
Mutate 00:00:00
Arrange 00:00:00
Group By And Summarise 00:00:00
Section 3 - Data Visualization In R
Basic Plots In R 00:00:00
Intro To ggplot2 And Creating Scatterplots 00:00:00
Boxplots 00:00:00
Barplots 00:00:00
Histograms 00:00:00
Faceted Graph 00:00:00
Section 4 - Statistics
Correlation Coefficients 00:00:00
Standard Deviations 00:00:00
Confidence Intervals 00:00:00
One Sample T-Test 00:00:00
Two Sample T-Test 00:00:00
Two Sample T-Test Of Proportions 00:00:00
ANOVA 00:00:00
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