Description
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.
Curriculum
Unit 01: Data Science Overview
Unit 02: R and RStudio
Unit 03: Introduction to Basics
Unit 04: Vectors
Unit 05: Matrices
Unit 06: Factors
Unit 07: Data Frames
Unit 08: Lists
Unit 09: Relational Operators
Unit 10: Logical Operators
Unit 11: Conditional Statements
Unit 12: Loops
Unit 13: Functions
Unit 14: R Packages
Unit 15: The Apply Family – lapply
Unit 16: The apply Family – sapply & vapply
Unit 17: Useful Functions
Unit 18: Regular Expressions
Unit 19: Dates and Times
Unit 20: Getting and Cleaning Data
Unit 21: Plotting Data in R
Unit 22: Data Manipulation with dplyr
