R Programming for Data Science

~~₦120000~~ ₦50000

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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.

We guarantee that all our online courses will meet or exceed your expectations. If you are not fully satisfied with a course - for any reason at all - simply request a full refund. We guarantee no hassles. That's our promise to you.

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Course Curriculum

Unit 01: Data Science Overview
	Introduction to Data Science
	Data Science: Career of the Future
	What is Data Science?
	Data Science as a Process
	Data Science Toolbox
	Data Science Process Explained
	What’s Next?
Unit 02: R and RStudio
	Engine and coding environment
	Installing R and RStudio
	RStudio: A quick tour
Unit 03: Introduction to Basics
	Arithmetic with R
	Variable assignment
	Basic data types in R
Unit 04: Vectors
	Creating a vector
	Naming a vector
	Arithmetic calculations on vectors
	Vector selection
	Selection by comparison
Unit 05: Matrices
	What’s a Matrix?
	Analyzing Matrices
	Naming a Matrix
	Adding columns and rows to a matrix
	Selection of matrix elements
	Arithmetic with matrices
	Additional Materials
Unit 06: Factors
	What’s a Factor?
	Categorical Variables and Factor Levels
	Summarizing a Factor
	Ordered Factors
Unit 07: Data Frames
	What’s a Data Frame?
	Creating Data Frames
	Selection of Data Frame elements
	Conditional selection
	Sorting a Data Frame
	Additional Materials
Unit 08: Lists
	Why would you need lists?
	Creating a List
	Selecting elements from a list
	Adding more data to the list
	Additional Materials
Unit 09: Relational Operators
	Equality
	Greater and Less Than
	Compare Vectors
	Compare Matrices
	Additional Materials
Unit 10: Logical Operators
	AND, OR, NOT Operators
	Logical operators with vectors and matrices
	Reverse the result: (!)
	Relational and Logical Operators together
	Additional Materials
Unit 11: Conditional Statements
	The IF statement
	IF…ELSE
	The ELSEIF statement
	Full Exercise
	Additional Materials
Unit 12: Loops
	Write a While loop
	Looping with more conditions
	Break: stop the While Loop
	What’s a For loop?
	Loop over a vector
	Loop over a list
	Loop over a matrix
	For loop with conditionals
	Using Next and Break with For loop
	Additional Materials
Unit 13: Functions
	What is a Function?
	Arguments matching
	Required and Optional Arguments
	Nested functions
	Writing own functions
	Functions with no arguments
	Defining default arguments in functions
	Function scoping
	Control flow in functions
	Additional Materials
Unit 14: R Packages
	Installing R Packages
	Loading R Packages
	Different ways to load a package
	Additional Materials
Unit 15: The Apply Family - lapply
	What is lapply and when is used?
	Use lapply with user-defined functions
	lapply and anonymous functions
	Use lapply with additional arguments
	Additional Materials
Unit 16: The apply Family – sapply & vapply
	What is sapply?
	How to use sapply
	sapply with your own function
	sapply with a function returning a vector
	When can’t sapply simplify?
	What is vapply and why is it used?
	Additional Materials
Unit 17: Useful Functions
	Mathematical functions
	Data Utilities
	Additional Materials
Unit 18: Regular Expressions
	grepl & grep
	Metacharacters
	sub & gsub
	More metacharacters
	Additional Materials
Unit 19: Dates and Times
	Today and Now
	Create and format dates
	Create and format times
	Calculations with Dates
	Calculations with Times
	Additional Materials
Unit 20: Getting and Cleaning Data
	Get and set current directory
	Get data from the web
	Loading flat files
	Loading Excel files
	Additional Materials
Unit 21: Plotting Data in R
	Base plotting system
	Base plots: Histograms
	Base plots: Scatterplots
	Base plots: Regression Line
	Base plots: Boxplot
Unit 22: Data Manipulation with dplyr
	Introduction to dplyr package
	Using the pipe operator (%>%)
	Columns component: select()
	Columns component: rename() and rename_with()
	Columns component: mutate()
	Columns component: relocate()
	Rows component: filter()
	Rows component: slice()
	Rows component: arrange()
	Rows component: rowwise()
	Grouping of rows: summarise()
	Grouping of rows: across()
	COVID-19 Analysis Task
	Additional Materials

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