9 Best R Programming Certification Courses - Learn R Programming Online
The Best R Programming Certification Courses online for beginners to learn R Programming in 2024.
If you want to participate in the data revolution, you need the right tools and skills. R is a free, open-source language for data science that is among the most popular platforms for professional analysts.
R Programming language is an incredible language for factual figuring and illustrations. It is prevalently utilized among analysts and data miners for creating measurable programming and performing information examinations. The adaptable language is free under the GNU General Public License and was created on the S Language and climate which was created at Bell Laboratories.
R Programming is the expertise you need on the off chance that you need to function as an information investigator or an information researcher in your industry of decision. Also, is there any good reason why you wouldn't? An information researcher is the most sizzling positioned calling in the US.
But to do that, you need the tools and the skill set to handle data. R is one of the top languages to get you where you want to be. Combine that with statistical know-how, and you will be well on your way to your dream title.
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Top R Programming Certification Courses List
1. R Programming A-Z: R For Data Science (Course & Exercises)
Learn Programming In R And R Studio. Data Analytics, Data Science, Statistical Analysis, Packages, Functions, GGPlot2.
- Course rating: 4.6 out of 5.0 (38,493 Rating total)
- Duration: 10.5 Hours
- Certificate: Certificate of completion
In this course, you will learn how to:
- program in R at a good level.
- use R Studio.
- understand the core principles of programming.
- create vectors in R.
- create variables.
- understand integer, double, logical, character, and other types in R.
- create a while() loop and a for() loop in R.
- build and use matrices in R.
- understand the matrix() function, learn rbind() and cbind().
- install packages in R.
- customize R studio to suit your preferences.
- understand the Law of Large Numbers.
- understand the Normal distribution.
- practice working with statistical data in R.
- practice working with financial data in R.
- practice working with sports data in R.
The course includes:
- Hit the Ground Running
- Core Programming Principles
- Fundamentals of R
- Matrices
- Data Frames
- Advanced Visualization with GGPlot 2
- Homework Solutions
You can take the R Programming A-Z: R For Data Science (Course & Exercises) Certificate Course on Udemy.
2. R Programming
Offered by Johns Hopkins University.
- Course rating: 4.5 out of 5.0 (20,398 Rating total)
- Duration: 57 Hours
- Certificate: Certificate of completion
In this course, you will learn how to:
- understand critical programming language concepts.
- configure statistical programming software.
- make use of R-loop functions and debugging tools.
- collect detailed information using the R profiler.
In this course, you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
The course covers practical issues in statistical computing which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code.
Initially, the course will cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. It will also cover key topics like control structures and functions.
Next, you will cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.
Finally, the course covers how to simulate data in R, which serves as the basis for doing simulation studies. It also covers the profiler in R which lets you collect detailed information on how your R functions are running and identify bottlenecks that can be addressed.
A profiler is a key tool in helping you optimize your programs. Lastly, the course will cover the str function, which is believed to be the most useful function in R.
3. R Programming: Advanced Analytics In R For Data Science
Take Your R & R Studio Skills To The Next Level. Data Analytics, Data Science, Statistical Analysis in Business, GGPlot2.
- Course rating: 4.7 out of 5.0 (6,662 Rating total)
- Duration: 6 Hours
- Certificate: Certificate of completion
In this course, you will learn how to:
- perform Data Preparation in R.
- identify missing records in data frames.
- locate missing data in your data frames.
- apply the Median Imputation method to replace missing records.
- apply the Factual Analysis method to replace missing records.
- use the which() function.
- reset the data frame index.
- work with the gsub() and sub() functions for replacing strings.
- understand NA is a third type of logical constant.
- deal with date times in R.
- convert date-times into POSIXct time format.
- create, use, append, modify, rename, access, and subset Lists in R.
- understand when to use [] and when to use [[]] or the $ sign when working with Lists.
- create a time series plot in R.
- understand how the Apply family of functions works.
- recreate an apply statement with a for a () loop.
- use apply() when working with matrices.
- use lapply() and sapply() when working with lists and vectors.
- add your own functions into apply statements.
- nest apply(), lapply() and sapply() functions within each other.
- use the which.max() and which.min() functions.
The course includes:
- Your Shortcut To Becoming A Better Data Scientist
- Data Preparation
- Lists in 'R'
- "Apply" Family of Functions
Here, you will learn how you can prepare data for analysis in R and perform the median imputation method in it. You will also learn how to work with date times in R.
Next, you will learn what Lists are and how to use them and what the Apply family of functions is. You will also learn how to use apply(), lapply(), and sapply() instead of loops.
Finally, you will learn how to nest your own functions within apply-type functions and also how to nest apply(), apply(), and sapply() functions within each other.
4. Learning R
Learn the basics of R, the free, open-source language for data science. Discover how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis.
- Course rating: 4.5 out of 5.0 (82,565 Rating total)
- Duration: 2.9 Hours
- Certificate: Certificate of completion
The course includes:
- R for Data Science
- R in context
- Getting Started with R
- Data Visualization
- Data Wrangling
- Data Analysis
Learn the basics of R and get started finding insights from your own data, in this course. The lessons explain how to get started with R, including installing R, RStudio, and code packages that extend R’s power.
You also see first-hand how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis.
By the end of the course, you will have a thorough introduction to the power and flexibility of R, and understand how to leverage this tool to explore and analyze a wide variety of data.
5. R Programming for Statistics and Data Science 2023
R Programming for Data Science & Data Analysis. Applying R for Statistics and Data Visualization with GGplot2 in R.
- Course rating: 4.5 out of 5.0 (2,886 Rating total)
- Duration: 6.5 Hours
- Certificate: Certificate of completion
In this course, you will learn how to:
- understand the fundamentals of programming in R.
- work with R’s conditional statements, functions, and loops.
- build your own functions in R.
- get your data in and out of R.
- understand the core tools for data science with R.
- manipulate data with the Tidyverse ecosystem of packages.
- Systematically explore data in R.
- understand the grammar of graphics and the ggplot2 package.
- visualize data: plot different types of data & draw insights.
- transform data: best practices of when and how.
- understand index, slice, and subset data.
- understand the fundamentals of statistics and apply them in practice.
- perform Hypothesis testing in R.
- understand and carry out regression analysis in R.
- work with dummy variables.
- make decisions that are supported by the data.
- have fun by taking apart Star Wars and Pokemon data, as well as some more serious data sets.
The course includes:
- Introduction to R
- Downloading and Installing R & R Studio
- The Building Blocks of R
- Vectors and Vector Operations
- Matrices
- Fundamentals of Programming with R
- Data Frames
- Manipulating Data
- Visualizing Data
- Exploratory Data Analysis
R for Statistics and Data Science is the course that will help you from a complete beginner in programming with R to a professional who can complete data manipulation on demand.
It will help you in acquiring the complete skill set to tackle a new data science project with confidence and be able to critically assess your work and others.
Next, the course will take you through descriptive statistics and the fundamentals of inferential statistics. You will do it in a step-by-step manner, incrementally building up your theoretical knowledge and practical skills.
Finally, you will master confidence intervals and hypothesis testing, as well as regression and cluster analysis. You will learn to work with vectors, matrices, data frames, and lists.
6. R Programming for Beginners
In this R programming tutorial, learn what is R Programming Language, the benefits of R, the Syntax of the language, and how to write programs in R language from scratch
- Course rating: 4.4 out of 5.0 (665 Rating total)
- Duration: 4 Hours
- Certificate: Certificate of completion
In this course, you will learn:
- what is R Programming Language
- what are the benefits of R.
- what is the syntax of the language?
- how to write programs in R language from scratch.
The course includes:
- Getting started with R
- Look and Feel of R
- Installation for R
- Variables and Packages
- Data Sets
- Charts and Statistics for one variable
- Working with Data including how to analyze the data
- Charts for Associations
- Statistics for Association
This course will break down the language into its core components to help make it easier to learn. It will help you learn what is R Programming Language, the benefits of R, the syntax of the language, and also how to write programs using this language.
The course has been designed for newbies, as well as intermediate students who want to go back to the basics and learn something new. It course focuses on the core aspects of the R programming language and how it can be used to work with data.
The course does not simply focus on the theory, but also on how to actually work with data by showing you the step-by-step process and helping you build your own experimental programs.
These will also help you get some insight into how you can actually write programs using R and how you can analyze data sets to create graphical representations of the data that you have.
At the end of this course, you will have the knowledge as well as the confidence to start working on analyzing large data sets and turning them into data that makes sense.
7. R Programming For Absolute Beginners
Learn the basics of writing code in R - your first step to becoming a data scientist.
- Course rating: 4.3 out of 5.0 (2,258 Rating total)
- Duration: 9.5 Hours
- Certificate: Certificate of completion
In this course, you will learn how to:
- work with vectors, matrices, and lists.
- work with factors.
- manage data frames.
- write complex programming structures (loops and conditional statements).
- build their own functions and binary operations.
- work with strings.
- create charts in base R.
The course includes:
- Introduction to R
- Getting Started with R
- Vectors
- Matrices and Arrays
- Lists
- Factors
- Data Frames
- Programming Structures
- Working with Strings
- Plotting in Base R
In the first section of this course, you will get started with R: you will install the program (in case you didn’t do it already), you will familiarize yourself with the working interface in R Studio and you will learn some basic technical stuff like installing and activating packages or setting the working directory.
Moreover, you will learn how to perform simple operations in R and how to work with variables. Next, you will learn about the five types of data structures in R: vectors, matrices, lists, factors, and data frames.
You will learn how to manipulate data structures: how to index them, how to edit data, how to filter data according to various criteria, how to create and modify objects (or variables), and how to apply functions to data.
These are very important topics because R is a software for statistical computing and most of the R programming is about manipulating data. So before getting to more advanced statistical analyses in R you must know the basic techniques of data handling.
After finishing with the data structures, you will get to the programming structures in R. In this section you will learn about loops, conditional statements, and functions. You will learn how to combine loops and conditional statements to perform complex tasks, and how to create custom functions that you can save and reuse later.
The next section is about working with strings. Here, you will cover the most useful functions that allow us to manipulate strings. So you will learn how to format strings for printing, how to concatenate strings, how to extract substrings from a given string, and especially how to create regular expressions that identify patterns in strings.
In the following section, you will learn how to build charts in R. The course is going to cover seven types of charts: dot chart (scatterplot), line chart, bar chart, pie chart, histogram, density line, and boxplot. Finally, you will learn how to plot a function of one variable and how to export the charts you create.
8. R Programming in Data Science: High-Volume Data
Analyze high-volume data using R, the language optimized for big data. Learn how to produce visualizations, implement parallel processing, and integrate with SQL and Apache Spark.
- Course rating: 24,960 total enrollments
- Duration: 1.4 Hours
- Certificate: Certificate of completion
The course includes:
- Introduction to R
- Problems and Opportunities with High-Volume Data
- Visualizing High-Volume Data
- Working within the R Programming Language
- Advanced High-Volume Techniques
- Use R with External Big Data Solutions
This course shows why R is ideal for high volumes of data, introduces more efficient ways to use the language, and explains how to avoid problems and capitalize on the opportunities of big data.
Learn how to determine if you have enough memory and processing power, produce visualizations of big data, optimize your R code, and use advanced techniques such as parallel processing to speed up your computations. Plus, discover how to integrate R with big-data solutions such as SQL databases and Apache Spark.
9. Introduction To Data Science Using R Programming
This data science using an R course is designed for beginners & intermediate developers to help them learn how to analyze, organize, & visualize data in this tutorial.
- Course rating: 4.4 out of 5.0 (633 Rating total)
- Duration: 7 Hours
- Certificate: Certificate of completion
In this course, you will learn how to:
- analyze, organize, & visualize data in R.
The course includes:
- Basic Data Visualization
- Advanced-Data Visualization
- Generating Maps using JSON Structures
- Implementation of Statistics
- Data Munging/Wrangling
- Data Manipulation - Import/Export of Data into CSV or Excel Format
This data science using an R course is designed for beginners & intermediate developers to help them learn how to analyze, organize, & visualize data in this tutorial.
In this course, you will focus on not only familiarize yourself with the R programming language's basic syntax but also the computing environment where you will learn exactly how to import data, organize the data, create charts and graphs, and also export data.
The course will start by helping you learn about basic data visualizations, after which you will progress onto more advanced concepts and visualization strategies, how to generate maps, implement statistics, clean the data, and how to import and export data.
At the end of this course, you will have mastered exactly how to clean and organize data as well as how to import and export data to R! This is the perfect course for anyone who is looking to make the jump into the world of Data Science.
Thank you for reading this. We hope our course curation would help you to pick the right course to learn R Programming. In case you want to explore more, you can take the free R Programming courses.
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