R Programming for Data Science

R Programming for Data Science

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OVERVIEW

The R Programming for Data Science is the best way for you to gain deep insight and knowledge of this topic. You will learn from industry experts and obtain an endorsed certificate after completing the course. Enrol now for a limited-time discounted price.

In this course students will be able to start their career in Data Science by learning and practicing the basics of R Language. Students will learn how to install and configure R and RStudio and how to create various data structures. They will solve simple data problems using various R methods, functions, and packages. Students will also understand and use different data gathering, manipulation, and plotting methods to best extract information from raw data. At the end of the course, students will deploy what they have learned in a COVID-19 analysis task.

Like all the courses of One Education, this R Programming for Data Science is designed with the utmost attention and thorough research. All the topics are broken down into easy to understand bite-sized modules that help our learners to understand each lesson very easily.

We don’t just provide courses at One Education; we provide a rich learning experience. After purchasing a course from One Education, you get complete 1-year access with tutor support. 

Our expert instructors are always available to answer all your questions and make your learning experience exquisite.

After completing the R Programming for Data Science, you will instantly get an e-certificate that will help you get jobs in the relevant field and will enrich your CV.

If you want to learn about this topic and achieve a certificate, you should consider this R Programming for Data Science from One Education.

We are very upfront and clear about all the costs of the course.

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

Certificate of Achievement

Endorsed Certificate of Achievement from the Quality Licence Scheme

After successfully completing the course, learners will be able to order an endorsed certificate as proof of their new achievement. Endorsed certificates can be ordered and get delivered to your home by post for only £109. There is an additional £10 postage charge for international students.

CPD Certification from One Education

After successfully completing the assessment of this course, you will qualify for the CPD Certificate from One Education as proof of your continued expert development. Certification is available in PDF format, at the cost of £9, or a hard copy can be sent to you via post, at the cost of £15.

Endorsement

This course has been endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. This course is not regulated by Ofqual and is not an endorsed lesson. One Education will be able to advise you on any further recognition, for example, progression routes into further and/or higher education. For further information, please visit the Learner FAQs on the Quality Licence Scheme website.

Method of Assessment

To assess your learning, you have to complete the assignment questions provided at the end of the course. You have to score at least 60% to pass the exam and to qualify for Quality Licence Scheme endorsed, and CPD endorsed certificates. 

After submitting the assignment, our expert tutor will assess your assignment and will give you feedback on your performance.

After passing the assignment exam, you will be able to apply for a certificate.

WHY STUDY THIS COURSE

Whether you’re an existing practitioner or aspiring expert, this course will enhance your expertise and boost your CV with key skills and an endorsed lesson attesting to your knowledge.

The R Programming for Data Science is open to all, with no formal entry requirements. All you need is a passion for learning, a good understanding of the English language, numeracy and IT, and to be over the age of 16.

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 2 weeks, 1 day
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