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R Programming for Data Science Course
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Highlights
On-Demand course
6 hours 32 minutes
All levels
Description
Overview
By enroling in R Programming for Data Science, you can kickstart your vibrant career and strengthen your profound knowledge. You can learn everything you need to know about the topic.
The R Programming for Data Science course includes all of the most recent information to keep you abreast of the employment market and prepare you for your future. The curriculum for this excellent R Programming for Data Science course includes modules at all skill levels, from beginner to expert. You will have the productivity necessary to succeed in your organisation once you have completed our R Programming for Data Science Program.
So enrol in our R Programming for Data Science course right away if you're keen to envision yourself in a rewarding career.
Description
Enroling in this R Programming for Data Science course can improve your R Programming for Data Science perspective, regardless of your skill levels in the R Programming for Data Science topics you want to master. If you're already a R Programming for Data Science expert, this peek under the hood will provide you with suggestions for accelerating your learning, including advanced R Programming for Data Science insights that will help you make the most of your time. This R Programming for Data Science course will act as a guide for you if you've ever wished to excel at R Programming for Data Science.
Why Choose Us?
This course is accredited by the CPD Quality Standards.
Lifetime access to the whole collection of the learning materials.
Online test with immediate results.
Enroling in the course has no additional cost.
You can study and complete the course at your own pace.
Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.
Will I Receive A Certificate Of Completion?
Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.
Who Is This Course For?
This R Programming for Data Science course is a great place to start if you're looking to start a new career in R Programming for Data Science field. This training is for anyone interested in gaining in-demand R Programming for Data Science proficiency to help launch a career or their business aptitude.
Requirements
The R Programming for Data Science course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of R Programming for Data Science, without any fuss.
Career Path
This R Programming for Data Science training will assist you develop your R Programming for Data Science ability, establish a personal brand, and present a portfolio of relevant talents. It will help you articulate a R Programming for Data Science professional story and personalise your path to a new career. Furthermore, developing this R Programming for Data Science skillset can lead to numerous opportunities for high-paying jobs in a variety of fields.
Order Your Certificate To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:
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 | ||
IFELSE | 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 | ||
Order Your Certificate | |||
Order Your Certificate | 00:00:00 |
About The Provider
NextGen Learning
London, United Kingdom
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