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R Programming for Data Science

R Programming for Data Science

By Janets

4.7(160)
  • 30 Day Money Back Guarantee
  • Completion Certificate
  • 24/7 Technical Support

Highlights

  • On-Demand course

  • 6 hours 32 minutes

  • All levels

Description

Register on the R Programming for Data Science today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.

The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.

Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion.

The R Programming for Data Science is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones.

The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!

What You Get With The R Programming for Data Science

  • Receive a e-certificate upon successful completion of the course

  • Get taught by experienced, professional instructors

  • Study at a time and pace that suits your learning style

  • Get instant feedback on assessments

  • 24/7 help and advice via email or live chat

  • Get full tutor support on weekdays (Monday to Friday)

Course Design

The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

You are taught through a combination of

  • Video lessons

  • Online study materials

Certification

After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post.

Who Is This Course For:

The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.

Requirements:

The online training is open to all students and has no formal entry requirements. To study the R Programming for Data Science, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16.

Course Content

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

About The Provider

Janets
Janets
London
4.7(160)
Janets is an online platform where learners come to learn, and evolve. From the very beginning, the aim of this platform was to create an ever-growing community of avid learners instead of just delivering formulaic education. Emphasising on making the learners equipped for the fu...
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