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R Programming for Data Science Course

R Programming for Data Science Course

  • 30 Day Money Back Guarantee
  • Completion Certificate
  • 24/7 Technical Support

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

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NextGen Learning
NextGen Learning
London, United Kingdom

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