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R Programming for Data Science

R Programming for Data Science

What secrets hide in your data? Unlock them with R programming – from basic syntax to advanced manipulation techniques in 22 expert units.

Enrolled Students: 4

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Overview of R Programming for Data Science

Join our R Programming for Data Science course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The R Programming for Data Science course has everything you need to get a great start in this sector.

Improving and moving forward is key to getting ahead personally. The R Programming for Data Science course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now!

Get a Quick Look at The Course Content:

This R Programming for Data Science Course will help you to learn:

This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this R Programming for Data Science. Enrol today and complete the course to achieve a certificate that can change your career forever.

Details

Perks of Learning with IOMH

One-to-one support from a dedicated tutor throughout your course.
Study online — whenever and wherever you want.
Instant digital/ PDF certificate.
100% money back guarantee.
12 months access.

Process of Evaluation

After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate.

Certificate of Achievement

Certificate of Completion – Digital / PDF Certificate

After completing the R Programming for Data Science course, you can order your CPD Accredited Digital/ PDF Certificate for £5.99.

Certificate of Completion – Hard copy Certificate

You can get the CPD Accredited Hard Copy Certificate

for £12.99.

Shipping Charges:

Inside the UK: £3.99
International: £10.99

Who Is This Course for?

This R Programming for Data Science is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.

On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.

This course has been developed with maximum flexibility and accessibility, making it ideal for people who don’t have the time to devote to traditional education.

Requirements

You don’t need any educational qualifications or experience to enrol in the R Programming for Data Science course.

Do note: You must be at least 16 years old to enrol.

Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course.

Career Path

The certification and skills you get from this R Programming for Data Science Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors.

Frequently Asked Questions

Q. How do I purchase a course?

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4. Once the payment is made, you will receive an email with the login credentials, and you can start learning after logging into the portal.

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You should receive your Digital Certificate within 24 hours after placing the order, and it will take 3-9 days to deliver the hard copies to your address if you are in the UK.

For International Delivery, it will take 20-25 days.

If you require any assistance, get in touch with our dedicated Customer Support team, and your queries/issues will be dealt with accordingly.

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You can make the payment using PayPal or you can Bank Transfer the amount.

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After that, you will receive an email with the invoice and bank details and you can make the payment accordingly.

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To resolve this issue, please log out of your account and then log back in.

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
Assignment
	Assignment – R Programming for Data Science	00:00:00