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R Programming for Data Science
( 6 Reviews )
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Introduction to Data Science 00:01:00 -
Data Science: Career of the Future 00:04:00
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What is Data Science? 00:02:00
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Data Science as a Process 00:02:00
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Data Science Toolbox 00:03:00
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Data Science Process Explained 00:05:00
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What’s Next? 00:01:00
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Engine and coding environment 00:03:00
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Installing R and RStudio 00:04:00
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RStudio: A quick tour 00:04:00
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Arithmetic with R 00:03:00
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Variable assignment 00:04:00
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Basic data types in R 00:03:00
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Creating a vector 00:05:00
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Naming a vector 00:04:00
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Arithmetic calculations on vectors 00:07:00
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Vector selection 00:06:00
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Selection by comparison 00:04:00
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What’s a Matrix? 00:02:00
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Analyzing Matrices 00:03:00
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Naming a Matrix 00:05:00
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Adding columns and rows to a matrix 00:06:00
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Selection of matrix elements 00:03:00
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Arithmetic with matrices 00:07:00
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Additional Materials
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What’s a Factor? 00:02:00
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Categorical Variables and Factor Levels 00:04:00
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Summarizing a Factor 00:01:00
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Ordered Factors 00:05:00
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What’s a Data Frame? 00:03:00
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Creating Data Frames 00:20:00
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Selection of Data Frame elements 00:03:00
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Conditional selection 00:03:00
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Sorting a Data Frame 00:03:00
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Additional Materials
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Why would you need lists? 00:01:00
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Creating a List 00:06:00
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Selecting elements from a list 00:03:00
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Adding more data to the list 00:02:00
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Additional Materials
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Equality 00:03:00
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Greater and Less Than 00:03:00
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Compare Vectors 00:03:00
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Compare Matrices 00:02:00
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Additional Materials
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AND, OR, NOT Operators 00:04:00
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Logical operators with vectors and matrices 00:04:00
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Reverse the result: (!) 00:01:00
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Relational and Logical Operators together 00:06:00
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Additional Materials
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The IF statement 00:04:00
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IF…ELSE 00:03:00
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The ELSEIF statement 00:05:00
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Full Exercise 00:03:00
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Additional Materials
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Write a While loop 00:04:00
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Looping with more conditions 00:04:00
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Break: stop the While Loop 00:04:00
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What’s a For loop? 00:02:00
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Loop over a vector 00:02:00
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Loop over a list 00:03:00
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Loop over a matrix 00:04:00
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For loop with conditionals 00:01:00
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Using Next and Break with For loop 00:03:00
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Additional Materials
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What is a Function? 00:02:00
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Arguments matching 00:03:00
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Required and Optional Arguments 00:03:00
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Nested functions 00:02:00
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Writing own functions 00:03:00
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Functions with no arguments 00:02:00
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Defining default arguments in functions 00:04:00
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Function scoping 00:02:00
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Control flow in functions 00:03:00
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Additional Materials
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Installing R Packages 00:01:00
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Loading R Packages 00:04:00
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Different ways to load a package 00:02:00
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Additional Materials
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What is lapply and when is used? 00:04:00
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Use lapply with user-defined functions 00:03:00
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lapply and anonymous functions 00:01:00
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Use lapply with additional arguments 00:04:00
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Additional Materials
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What is sapply? 00:02:00
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How to use sapply 00:02:00
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sapply with your own function 00:02:00
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sapply with a function returning a vector 00:02:00
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When can’t sapply simplify? 00:02:00
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What is vapply and why is it used? 00:04:00
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Additional Materials
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Mathematical functions 00:05:00
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Data Utilities 00:08:00
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Additional Materials
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grepl & grep 00:04:00
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Metacharacters 00:05:00
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sub & gsub 00:02:00
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More metacharacters 00:04:00
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Additional Materials
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Today and Now 00:02:00
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Create and format dates 00:06:00
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Create and format times 00:03:00
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Calculations with Dates 00:03:00
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Calculations with Times 00:07:00
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Additional Materials
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Get and set current directory 00:04:00
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Get data from the web 00:04:00
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Loading flat files 00:03:00
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Loading Excel files 00:05:00
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Additional Materials
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Base plotting system 00:03:00
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Base plots: Histograms 00:03:00
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Base plots: Scatterplots 00:05:00
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Base plots: Regression Line 00:03:00
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Base plots: Boxplot 00:03:00
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Introduction to dplyr package 00:04:00
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Using the pipe operator (%>%) 00:02:00
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Columns component: select() 00:05:00
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Columns component: rename() and rename_with() 00:02:00
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Columns component: mutate() 00:02:00
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Columns component: relocate() 00:02:00
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Rows component: filter() 00:01:00
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Rows component: slice() 00:04:00
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Rows component: arrange() 00:01:00
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Rows component: rowwise() 00:02:00
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Grouping of rows: summarise() 00:03:00
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Grouping of rows: across() 00:02:00
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COVID-19 Analysis Task 00:08:00
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Additional Materials
Course Overview
This premium QLS endorsed R Programming for Data Science course comes with intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunity and a globally recognised certification all-together. Thus, successful completion of our R Programming for Data Science course will give you limitless opportunities anywhere and everywhere, since the value of QLS endorsed courses are quite high, to say the least!!
What’s more, the quality of our first-class course is uncompromising for both you and us. You will be taught the richest of materials from this exquisite R Programming for Data Science course, enabling you to open up a vast world of opportunities for you to grasp and gain success from. For the price point, this course is actually an absolute steal! It’s an investment you absolutely do not want to miss out on!
So, why delay any longer? Benefit your career progression by joining us today and distinguish yourself as, unquestionably, the most skilled one in your workplace!
Key features
- E-learning and E-assessment system with flexibility.
- Learners get interactive resources.
- Expert advice from qualified professionals
- Globally recognised certificate.
- 14 days refund guarantee.
- 24/7 Tutor Support
Learning Outcome
- Have a thorough grasp of the R Programming for Data Science in just a few hours, rather than years.
- Acquainted yourself with the most recent industry developments and changes.
- Adjust and adapt in any working setting by combining your academic and practical expertise.
- Seek assistance from our qualified teachers at any moment.
- Get your hands on professionally developed and prepared course materials.
- Study whenever and from anywhere you wish.
Course Details
We, as one of the leading eLearning service providers across the globe, strive to provide all our learners with the best eLearning experience possible that can make a real difference in their career progression.
- 7+ hours of video lectures and digital resources are available on demand.
- Learn at your own speed with affordable, high-quality E-learning content.
- When you finish the course, you will be given a certificate of completion.
- Enhance your resume from a globally recognised Accredited Qualification.
- You’ll discover how effective salespeople use a researched and proven technique to boost their careers.
- You’ll be able to successfully implement several practical sales strategies and have a deeper understanding of your consumers.
Our course is open for everyone. This R Programming for Data Science is ideal for those who aspire to gain in-depth knowledge & efficient skill to embark into the relevant sector as a promising potential.
- Unit 01: Data Science Overview
- Unit 02: R and RStudio
- Unit 03: Introduction to Basics
- Unit 04: Vectors
- Unit 05: Matrices
- Unit 06: Factors
- Unit 07: Data Frames
- Unit 08: Lists
- Unit 09: Relational Operators
- Unit 10: Logical Operators
- Unit 11: Conditional Statements
- Unit 12: Loops
- Unit 13: Functions
- Unit 14: R Packages
- Unit 15: The Apply Family – lapply
- Unit 16: The apply Family – sapply & vapply
- Unit 17: Useful Functions
- Unit 18: Regular Expressions
- Unit 19: Dates and Times
- Unit 20: Getting and Cleaning Data
- Unit 21: Plotting Data in R
- Unit 22: Data Manipulation with dplyr
Learners must submit a comprehensive summerised assignment on all the units that will be assessed by our expert tutor if all the learning outcomes are met as per the standard set by QLS. After the quality check, learner will be provided with the R Programming for Data Science certificate.
An endorsed certificate will be issued to learners at the end of the course as recognition of course completion. Provided that learner completes all the assessment of a course can claim for certificate.
QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions including your progression routes into further higher education. It is a non-accredited course.
There are no formal requirements needed for this R Programming for Data Science course. So learners do not require any prior qualifications to enrol in this course.
Course Reviews
No Reviews found for this course.
Course Info
Lessons : 129
Assignments : 1
Duration : 6 hours, 32 minutes
Access : 1 year
Enrol now, pay later in easy instalments(Interest free) with Clearpay.
