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Course Highlights
Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Data Science: R Programming Online Course has been specially designed to help learners gain a good command of Data Science: R Programming Online Course, providing them with a solid foundation of knowledge to become a qualified professional.
Through this Data Science: R Programming Online Course, you will gain both practical and theoretical understanding of Data Science: R Programming Online Course that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.
Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.
Learning outcome
- Familiar yourself with the recent development and updates of the relevant industry
- Know how to use your theoretical knowledge to adapt in any working environment
- Get help from our expert tutors anytime you need
- Access to course contents that are designed and prepared by industry professionals
- Study at your convenient time and from wherever you want
Course media
Why should I take this course?
- Affordable premium-quality E-learning content, you can learn at your own pace.
- You will receive a completion certificate upon completing the course.
- Internationally recognized Accredited Qualification will boost up your resume.
- You will learn the researched and proven approach adopted by successful people to transform their careers.
- You will be able to incorporate various techniques successfully and understand your customers better.
Requirements
- No formal qualifications required, anyone from any academic background can take this course.
- Access to a computer or digital device with internet connectivity.
Curriculum
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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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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 matrices
00:07:00
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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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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?
00:04:00
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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 Materials15
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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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Mathematical functions
00:05:00
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Data Utilities
00:08:00
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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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More metacharacters
00:04:00
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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
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Duration:6 hours, 50 minutes
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Access:1 Year
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Units:129
