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R Programming for Data Science
By Studyhub UK
(3)- 30 Day Money Back Guarantee
- Completion Certificate
- 24/7 Technical Support
Highlights
On-Demand course
6 hours 33 minutes
All levels
Description
Delve into the world of data analysis with 'R Programming for Data Science,' a course designed to guide learners through the intricacies of R, a premier programming language in the data science domain. The course opens with a broad perspective on data science, illuminating the pivotal role of R in this field. Learners are then introduced to R and RStudio, equipping them with the foundational tools and interfaces essential for R programming. The curriculum progresses with an introduction to the basics of R, ensuring learners grasp the core principles that underpin more complex operations.
A highlight of this course is its in-depth exploration of R's versatile data structures, including vectors, matrices, factors, and data frames. Each unit is crafted to provide learners with a comprehensive understanding of these structures, pivotal for effective data handling and manipulation. The course also emphasizes the importance of relational and logical operators in R, key elements for executing data operations. As the course advances, learners will engage with the nuances of conditional statements and loops, essential for writing efficient and dynamic R scripts.
Moving into more advanced territories, the course delves into the creation and usage of functions, an integral part of R programming, and the exploration of various R packages that extend the language's capabilities. Learners will also gain expertise in the 'apply' family of functions, crucial for streamlined data processing. Further units cover regular expressions and effective strategies for managing dates and times in data sets. The course concludes with practical applications in data acquisition, cleaning, visualization, and manipulation, ensuring learners are well-prepared to tackle real-world data science challenges using R.
Learning Outcomes
Develop a foundational understanding of R's role in data science and proficiency in RStudio.
Gain fluency in R programming basics, enabling the handling of complex data tasks.
Acquire skills in managing various R data structures for efficient data analysis.
Master relational and logical operations for advanced data manipulation in R.
Learn to create functions and utilize R packages for expanded analytical capabilities.
Why choose this R Programming for Data Science course?
Unlimited access to the course for a lifetime.
Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course.
Structured lesson planning in line with industry standards.
Immerse yourself in innovative and captivating course materials and activities.
Assessments designed to evaluate advanced cognitive abilities and skill proficiency.
Flexibility to complete the Course at your own pace, on your own schedule.
Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.
Unlock career resources for CV improvement, interview readiness, and job success.
Who is this R Programming for Data Science course for?
Beginners in data science eager to learn R programming.
Data analysts and scientists looking to enhance their skills in R.
Researchers in various fields requiring advanced data analysis tools.
Statisticians seeking to adopt R for more sophisticated data manipulations.
Professionals in finance, healthcare, and other sectors needing data-driven insights.
Career path
Data Scientist (R Expertise): £30,000 - £70,000
Data Analyst (R Programming Skills): £27,000 - £55,000
Bioinformatics Scientist (R Proficiency): £35,000 - £60,000
Quantitative Analyst (R Knowledge): £40,000 - £80,000
Research Analyst (R Usage): £25,000 - £50,000
Business Intelligence Developer (R Familiarity): £32,000 - £65,000
Prerequisites
This R Programming for Data Science does not require you to have any prior qualifications or experience. You can just enrol and start learning.This R Programming for Data Science was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.
Certification
After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.
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? | 00:02:00 | ||
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 | ||
IFELSE | 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 |
About The Provider
Studyhub UK
London, England
(3)Studyhub is a premier online learning platform which aims to help individuals worldwide to realise their educational dreams. For 5 years, we have been dedicated...
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