Name: R Programming for Data Science
Price: 26.99 GBP
Rating: 4.5 (59 reviews)

R Programming for Data Science

Overview The R Programming for Data Science course provides a comprehensive introduction to the R programming language, focusing on its …

R Programming for Data Science

Original price was: $417.25.Current price is: $35.30.

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Intermediate

29 Students

6 hours, 25 minutes

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R Programming for Data Science Overview

The R Programming for Data Science course provides a comprehensive introduction to the R programming language, focusing on its applications in data science. This course covers fundamental programming concepts, data manipulation techniques, and advanced topics essential for data analysis. Participants will learn how to use R and RStudio, manipulate data structures, perform data cleaning, and create visualisations. By the end of this course, students will be equipped with the skills necessary to tackle real-world data science challenges using R.

Learning Outcomes

Understand the basic concepts of data science and the role of R in data analysis.
Navigate and utilise R and RStudio for data science tasks.
Master basic R syntax and data types, including vectors, matrices, factors, and data frames.
Apply relational and logical operators for data manipulation.
Implement conditional statements and loops to control the flow of programmes.
Create and use functions to streamline repetitive tasks.
Explore and utilise R packages to extend R’s functionality.
Employ the Apply family of functions (lapply, sapply, vapply) for efficient data processing.
Use regular expressions for pattern matching and text manipulation.
Perform data cleaning, manipulation with dplyr, and data visualisation in R.

Who Is This Course For

This course is designed for individuals who are interested in pursuing a career in data science or enhancing their data analysis skills. It is ideal for beginners with no prior experience in R programming, as well as professionals seeking to expand their expertise in data manipulation and visualisation using R. Whether you are a student, an aspiring data scientist, or a professional looking to leverage data science in your field, this course will provide you with the necessary tools and knowledge to succeed.

Eligibility Requirements

Participants should understand basic programming concepts and be familiar with data analysis. While you don’t need prior experience in R, having a foundational knowledge of mathematics and statistics will be beneficial. Bring your passion for learning and interest in data science to make the most out of this course.

Entry Requirements

Age Requirement: Applicants must be aged 16 or above, allowing both young learners and adults to engage in this educational pursuit.
Academic Background: There are no specific educational prerequisites, opening the door to individuals from diverse academic histories.
Language Proficiency: A good command of the English language is essential for comprehension and engagement with the course materials.
Numeracy Skills: Basic numeracy skills are required to effectively understand and work with course-related information.

Why Choose Us

Affordable, engaging & high-quality e-learning study materials;
Tutorial videos/materials from the industry-leading experts;
Study in a user-friendly, advanced online learning platform;
Efficient exam systems for the assessment and instant result;
The UK & internationally recognised accredited
Access to course content on mobile, tablet or desktop from anywhere, anytime;
The benefit of career advancement opportunities;
24/7 student support via email.

Career Path

Completing this course opens up various career opportunities in the field of data science and analytics. With the skills acquired, you can pursue roles such as data analyst, data scientist, business analyst, and more. Additionally, the knowledge gained from this course will enable you to contribute to data-driven decision-making processes within organisations, making you a valuable asset in today’s data-centric world. The foundational skills in R programming and data manipulation will also pave the way for advanced studies and specialisations in data science.

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
	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
Assignment
	Assignment – R Programming for Data Science	00:00:00
Order Certificate
	Order Certificate	00:00:00

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R Programming for Data Science

R Programming for Data Science

R Programming for Data Science Overview

Learning Outcomes

Who Is This Course For

Why Choose Us

Career Path

Course Curriculum

Course Ratings

N.A

How Do Our Courses Work?

Purchase and payment

Course access

Certificate

Continued support

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BLACK FRIDAY SALE : ALL COURSES FOR $652.69 Original price was: $652.69.$64.09Current price is: $64.09. / year

BLACK FRIDAY SALE : ALL COURSES FOR Original price was: $652.69.Current price is: $64.09. / year