Name: R Programming for Data Science
Price: 25 GBP

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Curriculum

Overview:

Welcome to “R Programming for Data Science”! This course is your gateway to mastering R, a powerful programming language and environment for statistical computing and data analysis. R is widely used by data scientists, statisticians, and researchers for its extensive range of libraries and packages tailored for data manipulation, visualization, and modeling. In this course, you’ll learn the fundamentals of R programming and how to leverage its capabilities for data science tasks.

Interactive video lectures by industry experts
Instant e-certificate and hard copy dispatch by next working day
Fully online, interactive course with Professional voice-over
Developed by qualified first aid professionals
Self paced learning and laptop, tablet, smartphone friendly
24/7 Learning Assistance
Discounts on bulk purchases

Main Course Features:

Comprehensive coverage of R programming fundamentals and syntax
Hands-on projects and exercises for practical application of concepts
Exploration of key R libraries and packages for data manipulation and analysis (e.g., dplyr, ggplot2)
Introduction to statistical analysis techniques using R
Implementation of machine learning algorithms for predictive modeling and pattern recognition
Real-world case studies and examples demonstrating R’s application in data science projects
Access to resources and tools for continued learning and practice in R programming
Supportive online community for collaboration and assistance throughout the course

Who Should Take This Course:

Data scientists, statisticians, and researchers looking to enhance their skills in R programming for data science tasks
Analysts and professionals seeking to transition into a career in data science
Students studying statistics, data analysis, or related fields interested in learning R for practical applications
Anyone interested in leveraging R for data manipulation, visualization, and modeling in their personal or professional projects

Learning Outcomes:

Master R programming fundamentals and syntax for data manipulation and analysis
Understand key R libraries and packages for statistical computing and data visualization
Apply statistical techniques to analyze and interpret data effectively using R
Develop machine learning models for predictive modeling tasks using R
Gain hands-on experience through projects and exercises in R programming
Build a portfolio of data science projects showcasing your proficiency in R
Communicate findings and insights effectively through data visualization and storytelling in R
Continue learning and exploring advanced topics in R programming and data science beyond the course curriculum.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.

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