R Programming for Data Science

Overview

Grasp the science of R Programming for Data Science and how it relates to launching a lucrative career. Prepare for future R Programming for Data Science needs by looking beyond the ongoing R Programming for Data Science impact with Discover Training’s up-to-date R Programming for Data Science course .

Discover Training’s R Programming for Data Science course gives you a solid foundation in R Programming for Data Science in many settings of human development. With industry expert interviews, you’ll gain a greater understanding of R Programming for Data Science‘s applications and limitations, as well as how it’s already been efficiently used in the field. Discover Training’s R Programming for Data Science course will help you obtain facts and knowledge to strengthen your abilities on both standard R Programming for Data Science techniques.

As you progress through the interactive modules of this online program, you will have a strong grasp of R Programming for Data Science to identify new opportunities for innovation and drive digital growth within your organization. Enrol in the R Programming for Data Science course today and leverage your knowledge tomorrow.

Description:

Industry professionals developed the content of this online R Programming for Data Science course, combining their expertise and in-depth knowledge. The curriculum was developed by Discover Training to assist you in growing in your current role by providing you with the necessary information and concepts. As you progress through the modules of this course, you’ll learn the fundamentals of R Programming for Data Science and explore the essential concepts.

Who is this course for?

This R Programming for Data Science course is for you if you want to learn about the revolutionary potential of R Programming for Data Science. Whether you are a newbie or a seasoned veteran, a grasp of challenges that R Programming for Data Science techniques can tackle would benefit anyone working in the R Programming for Data Science sector. This R Programming for Data Science training covers crucial subjects that will help you overcome challenges and improve your practices.

Requirements:

Certification

Once the R Programming for Data Science course is successfully completed, the learner can obtain the PDF certificate without any cost. The hardcopy version of the certificate is also available for only £9.99. However, anyone ordering the hardcopy certificate from outside the United Kingdom may need to pay an additional delivery charge.

Course Curriculum

Unit 01: Data Science Overview
	Introduction to Data Science	00:01:00
	Data Science: Career of the Future	00:04:00
	What is Data Science?	00:02:00
	Data Science as a Process	00:02:00
	Data Science Toolbox	00:03:00
	Data Science Process Explained	00:05:00
	What’s Next?	00:01:00
Unit 02: R and RStudio
	Engine and coding environment	00:03:00
	Installing R and RStudio	00:04:00
	RStudio: A quick tour	00:04:00
Unit 03: Introduction to Basics
	Arithmetic with R	00:03:00
	Variable assignment	00:04:00
	Basic data types in R	00:03:00
Unit 04: Vectors
	Creating a vector	00:05:00
	Naming a vector	00:04:00
	Arithmetic calculations on vectors	00:07:00
	Vector selection	00:06:00
	Selection by comparison	00:04:00
Unit 05: Matrices
	What’s a Matrix?	00:02:00
	Analyzing Matrices	00:03:00
	Naming a Matrix	00:05:00
	Adding columns and rows to a matrix	00:06:00
	Selection of matrix elements	00:03:00
	Arithmetic with matrices	00:07:00
	Additional Materials	00:00:00
Unit 06: Factors
	What’s a Factor?	00:02:00
	Categorical Variables and Factor Levels	00:04:00
	Summarizing a Factor	00:01:00
	Ordered Factors	00:05:00
Unit 07: Data Frames
	What’s a Data Frame?	00:03:00
	Creating Data Frames	00:20:00
	Selection of Data Frame elements	00:03:00
	Conditional selection	00:03:00
	Sorting a Data Frame	00:03:00
	Additional Materials	00:00:00
Unit 08: Lists
	Why would you need lists?	00:01:00
	Creating a List	00:06:00
	Selecting elements from a list	00:03:00
	Adding more data to the list	00:02:00
	Additional Materials	00:00:00
Unit 09: Relational Operators
	Equality	00:03:00
	Greater and Less Than	00:03:00
	Compare Vectors	00:03:00
	Compare Matrices	00:02:00
	Additional Materials	00:00:00
Unit 10: Logical Operators
	AND, OR, NOT Operators	00:04:00
	Logical operators with vectors and matrices	00:04:00
	Reverse the result: (!)	00:01:00
	Relational and Logical Operators together	00:06:00
	Additional Materials	00:00:00
Unit 11: Conditional Statements
	The IF statement	00:04:00
	IF…ELSE	00:03:00
	The ELSEIF statement	00:05:00
	Full Exercise	00:03:00
	Additional Materials	00:00:00
Unit 12: Loops
	Write a While loop	00:04:00
	Looping with more conditions	00:04:00
	Break: stop the While Loop	00:04:00
	What’s a For loop?	00:02:00
	Loop over a vector	00:02:00
	Loop over a list	00:03:00
	Loop over a matrix	00:04:00
	For loop with conditionals	00:01:00
	Using Next and Break with For loop	00:03:00
	Additional Materials	00:00:00
Unit 13: Functions
	What is a Function?	00:02:00
	Arguments matching	00:03:00
	Required and Optional Arguments	00:03:00
	Nested functions	00:02:00
	Writing own functions	00:03:00
	Functions with no arguments	00:02:00
	Defining default arguments in functions	00:04:00
	Function scoping	00:02:00
	Control flow in functions	00:03:00
	Additional Materials	00:00:00
Unit 14: R Packages
	Installing R Packages	00:01:00
	Loading R Packages	00:04:00
	Different ways to load a package	00:02:00
	Additional Materials	00:00:00
Unit 15: The Apply Family - lapply
	What is lapply and when is used?	00:04:00
	Use lapply with user-defined functions	00:03:00
	lapply and anonymous functions	00:01:00
	Use lapply with additional arguments	00:04:00
	Additional Materials	00:00:00
Unit 16: The apply Family – sapply & vapply
	What is sapply?	00:02:00
	How to use sapply	00:02:00
	sapply with your own function	00:02:00
	sapply with a function returning a vector	00:02:00
	When can’t sapply simplify?	00:02:00
	What is vapply and why is it used?	00:04:00
	Additional Materials	00:00:00
Unit 17: Useful Functions
	Mathematical functions	00:05:00
	Data Utilities	00:08:00
	Additional Materials	00:00:00
Unit 18: Regular Expressions
	grepl & grep	00:04:00
	Metacharacters	00:05:00
	sub & gsub	00:02:00
	More metacharacters	00:04:00
	Additional Materials	00:00:00
Unit 19: Dates and Times
	Today and Now	00:02:00
	Create and format dates	00:06:00
	Create and format times	00:03:00
	Calculations with Dates	00:03:00
	Calculations with Times	00:07:00
	Additional Materials	00:00:00
Unit 20: Getting and Cleaning Data
	Get and set current directory	00:04:00
	Get data from the web	00:04:00
	Loading flat files	00:03:00
	Loading Excel files	00:05:00
	Additional Materials	00:00:00
Unit 21: Plotting Data in R
	Base plotting system	00:03:00
	Base plots: Histograms	00:03:00
	Base plots: Scatterplots	00:05:00
	Base plots: Regression Line	00:03:00
	Base plots: Boxplot	00:03:00
Unit 22: Data Manipulation with dplyr
	Introduction to dplyr package	00:04:00
	Using the pipe operator (%>%)	00:02:00
	Columns component: select()	00:05:00
	Columns component: rename() and rename_with()	00:02:00
	Columns component: mutate()	00:02:00
	Columns component: relocate()	00:02:00
	Rows component: filter()	00:01:00
	Rows component: slice()	00:04:00
	Rows component: arrange()	00:01:00
	Rows component: rowwise()	00:02:00
	Grouping of rows: summarise()	00:03:00
	Grouping of rows: across()	00:02:00
	COVID-19 Analysis Task	00:08:00
	Additional Materials	00:00:00

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