2
R Programming for Data Science
CPD Accredited Course

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1

1

Students

GET THIS COURSE AND 1500+ OTHERS FOR ONLY £149. FIND OUT MORE

Course Overview

Gear up to get well equipped in R Programming for Data Science now, make up your mind to be the best in the business! All you need is proper training, firm support and push to shine in your vocation, and Compliance Central is determined to provide you with it all! Explore what you got in this exclusive R Programming for Data Science, start learning and excel in it! This amazing R Programming for Data Science course has been designed and developed by the industry specialists who had been to the business for years, went through ups-and-downs, climbed up the success ladder with sheer excellence! You’ll get to-the-point knowledge both practical and theoretical, and gain valuable insights on the business which will help you understand the drill better than ever! Join today, be skilled, learn with positive energy and enthusiasm, create an excellent career using your full potential! Brace yourself, enrol now for an amazing venture!

This R Programming for Data Science Course Package Includes

  • Comprehensive lessons and training provided by experts on R Programming for Data Science
  • Interactive online learning experience provided by qualified professionals in your convenience
  • 24/7 Access to the course materials and learner assistance
  • Easy accessibility from any smart device (Laptop, Tablet, Smartphone etc.)
  • A happy and handy learning experience for the professionals and students
  • 100% learning satisfaction, guaranteed by Compliance Central

Learning Outcome

Upon successful completion of this highly appreciated R Programming for Data Science course, you’ll be a skilled professional, besides—
  • You can provide services related to R Programming for Data Science with complete knowledge and confidence
  • You’ll be competent and proficient enough to start a R Programming for Data Science related business on your own
  • You can train up others and grow an efficient peer community on your locality and serve people
  • It will enhance your portfolio, you can use the certificate as proof of your efficiency to the employer
  • It will boost up your productivity, you can use the skill and credentials, and become more competent in your vocation with increased earning!

Certification

You can instantly download your certificate for £4.79 right after finishing the R Programming for Data Science course. The hard copy of the certification will also be sent right at your doorstep via post for £10.79. All of our courses are continually reviewed to ensure their quality, and that provide appropriate current training for your chosen subject. As such, although certificates do not expire, it is recommended that they are reviewed or renewed on an annual basis.

Who Is This Course For

Compliance Central aims to prepare efficient human resources for the industry and make it more productive than ever. This helpful course is suitable for any person who is interested in R Programming for Data Science. There are no pre-requirements to take it. You can attend the course if you are a student, an enthusiast or a
  • Employee
  • Employer
  • Manager
  • Supervisor
  • Entrepreneur
  • Business Professional
  • Company Leader
  • HR Professional

Course Currilcum

    • 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
    • Engine and coding environment 00:03:00
    • Installing R and RStudio 00:04:00
    • RStudio: A quick tour 00:04:00
    • Arithmetic with R 00:03:00
    • Variable assignment 00:04:00
    • Basic data types in R 00:03:00
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • Mathematical functions 00:05:00
    • Data Utilities 00:08:00
    • Additional Materials 00:00:00
    • grepl & grep 00:04:00
    • Metacharacters 00:05:00
    • sub & gsub 00:02:00
    • More metacharacters 00:04:00
    • Additional Materials 00:00:00
    • 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
    • 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
    • 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
    • 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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  • calendar Intermediate
  • student 1 students
  • clock 6 hours, 32 minutes
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Student Reviews

Ben lim

Gaining improve knowledge in the construction project management and the course is easy to understand.

Mr Brian Joseph Keenan

Very good and informative and quick with marking my assignments and issuing my certificate.

Sarah D

Being a support worker I needed add a child care cert in my portfolio. I have done the course and that was really a good course.

Sam Ryder

The first aid course was very informative with well organised curriculum. I already have some bit and pieces knowledge of first aid, this course helped me a lot.

Ben lim

Gaining improve knowledge in the construction project management and the course is easy to understand.

Thelma Gittens

Highly recommended. The module is easy to understand and definitely the best value for money. Many thanks

BF Carey

First course with Compliance Central. It was a good experience.

Course Currilcum

    • 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
    • Engine and coding environment 00:03:00
    • Installing R and RStudio 00:04:00
    • RStudio: A quick tour 00:04:00
    • Arithmetic with R 00:03:00
    • Variable assignment 00:04:00
    • Basic data types in R 00:03:00
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • Mathematical functions 00:05:00
    • Data Utilities 00:08:00
    • Additional Materials 00:00:00
    • grepl & grep 00:04:00
    • Metacharacters 00:05:00
    • sub & gsub 00:02:00
    • More metacharacters 00:04:00
    • Additional Materials 00:00:00
    • 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
    • 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
    • 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
    • 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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