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Learn how to make a genuine difference in your life by taking our popular R Programming for Data Science Course. Our commitment to online learning and our technical experience has been put to excellent use within the content of these educational modules. By enrolling today, you can take your knowledge of R Programming for Data Science to a whole new level and quickly reap the rewards of your study in the field you have chosen.
We are confident that you will find the skills and information that you will need to succeed in this area and excel in the eyes of others. Do not rely on substandard training or half-hearted education. Commit to the best, and we will help you reach your full potential whenever and wherever you need us.
Please note that R Programming for Data Science provides valuable and significant theoretical training for all. However, it does not offer official qualifications for professional practice. Always check details with the appropriate authorities or management.
By completing the training in R Programming for Data Science, you will be able to significantly demonstrate your acquired abilities and knowledge of R Programming for Data Science. This can give you an advantage in career progression, job applications, and personal mastery in this area.
This course is designed to provide an introduction to R Programming for Data Science and offers an excellent way to gain the vital skills and confidence to start a successful career. It also provides access to proven educational knowledge about the subject and will support those wanting to attain personal goals in this area. Full-time and part-time learners are equally supported, and the study periods are entirely customisable to your needs.
Once you have completed all the modules in the R Programming for Data Science course, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.
Show off Your New Skills with a Certification of Completion
The learners have to successfully complete the assessment of this R Programming for Data Science course to achieve the CPD accredited certificate. Digital certificates can be ordered for only £10. Learners can purchase printed hard copies inside the UK for £29, and international students can purchase printed hard copies for £39.
Unit 01: Data Science Overview | |||
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 | 00:05:00 | ||
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 | 00:05:00 | ||
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 | 00:04:00 | ||
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 | 00:06:00 | ||
Full Exercise | 00:04:00 | ||
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 | 00:05:00 | ||
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 | 00:04:00 | ||
Defining default arguments in functions | 00:04:00 | ||
Function scoping | 00:02:00 | ||
Control flow in functions | 00:06:00 | ||
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? | 00:03:00 | ||
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 | 00:05:00 | ||
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 | ||
Introduction to dplyr package | 00:04:00 | ||
Unit 22: Data Manipulation with dplyr | |||
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() | 00:03:00 | ||
Rows component: slice() | 00:04:00 | ||
Rows component: arrange() | 00:01:00 | ||
Rows component: rowwise() | 00:02:00 | ||
Grouping of rows: summarise() | 00:04:00 | ||
Grouping of rows: across() | 00:02:00 | ||
COVID-19 Analysis Task | 00:09:00 | ||
Additional Materials | 00:00:00 | ||
Assignment | |||
Assignment – R Programming for Data Science | 00:00:00 |
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