Overview
This comprehensive course on R Programming for Data Science will deepen your understanding on this topic.
After successful completion of this course you can acquire the required skills in this sector. This R Programming for Data Science comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.
So enrol in this course today to fast track your career ladder.
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is This course for?
There is no experience or previous qualifications required for enrolment on this R Programming for Data Science. It is available to all students, of all academic backgrounds.
Requirements
Our R Programming for Data Science is fully compatible with PC’s, Mac’s, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.
There is no time limit for completing this course, it can be studied in your own time at your own pace.
Career Path
Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc.
Course Curriculum
- 24 sections
- 48 lectures
- 00:00:00 total length
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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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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 00:00:00
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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 00:00:00
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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 00:00:00
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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 00:00:00
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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 00:00:00
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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 00:00:00
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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 00:00:00
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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 00:00:00
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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 00:00:00
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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 00:00:00
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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 00:00:00
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Mathematical functions 00:05:00
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Data Utilities 00:08:00
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Additional Materials 00:00:00
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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 00:00:00
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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 00:00:00
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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 00:00:00
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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 00:00:00
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Assignment – R Programming for Data Science 00:00:00
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Claim Your Certificate 00:00:00
R Programming for Data Science
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ALL COURSES FOR - £99 This course includes:
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Duration:6 hours, 25 minutes
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Access:1 year access
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Level:
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CPD Points:10
