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
- 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?
- Engine and coding environment
- Installing R and RStudio
- RStudio: A quick tour
- Arithmetic with R
- Variable assignment
- Basic data types in R
- Creating a vector
- Naming a vector
- Arithmetic calculations on vectors
- Vector selection
- Selection by comparison
- 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
- What’s a Factor?
- Categorical Variables and Factor Levels
- Summarizing a Factor
- Ordered Factors
- What’s a Data Frame?
- Creating Data Frames
- Selection of Data Frame elements
- Conditional selection
- Sorting a Data Frame
- Additional Materials
- Why would you need lists?
- Creating a List
- Selecting elements from a list
- Adding more data to the list
- Additional Materials
- Equality
- Greater and Less Than
- Compare Vectors
- Compare Matrices
- Additional Materials
- AND, OR, NOT Operators
- Logical operators with vectors and matrices
- Reverse the result: (!)
- Relational and Logical Operators together
- Additional Materials
- The IF statement
- IF…ELSE
- The ELSEIF statement
- Full Exercise
- Additional Materials
- 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
- 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
- Installing R Packages
- Loading R Packages
- Different ways to load a package
- Additional Materials
- What is lapply and when is used?
- Use lapply with user-defined functions
- lapply and anonymous functions
- Use lapply with additional arguments
- Additional Materials
- 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
- Mathematical functions
- Data Utilities
- Additional Materials
- grepl & grep
- Metacharacters
- sub & gsub
- More metacharacters
- Additional Materials
- Today and Now
- Create and format dates
- Create and format times
- Calculations with Dates
- Calculations with Times
- Additional Materials
- Get and set current directory
- Get data from the web
- Loading flat files
- Loading Excel files
- Additional Materials
- Base plotting system
- Base plots: Histograms
- Base plots: Scatterplots
- Base plots: Regression Line
- Base plots: Boxplot
- 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
Frequently Asked Questions
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