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  • R Programming

R Courses

R courses can help you learn data manipulation, statistical analysis, data visualization, and programming fundamentals. You can build skills in creating reproducible research, performing regression analysis, and implementing machine learning algorithms. Many courses introduce tools like RStudio and packages such as ggplot2 and dplyr, which enhance your ability to analyze datasets and present findings effectively.


Popular R Courses and Certifications


  • C

    Coursera

    Getting Started with R

    Skills you'll gain: R (Software), Data Import/Export, Package and Software Management, R Programming, Data Structures, Software Installation, Data Analysis Software, Data Analysis, Data Science

    ★ 4.3 (384) · Beginner · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
  • C

    Coursera

    Introduction to R: Basic R syntax

    Skills you'll gain: R (Software), R Programming, Computational Logic, Arithmetic, Statistical Programming, Development Environment, Programming Principles, Data Import/Export

    ★ 4.5 (51) · Beginner · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
  • C

    Coursera

    Data Visualization using dplyr and ggplot2 in R

    Skills you'll gain: Ggplot2, Plot (Graphics), Tidyverse (R Package), Data Visualization, Statistical Visualization, Data Analysis, Exploratory Data Analysis, R Programming, Data Visualization Software, Data Wrangling, Data Manipulation, R (Software)

    ★ 5 (20) · Intermediate · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
  • C

    Coursera

    Data Analysis in R: Predictive Analysis with Regression

    Skills you'll gain: Ggplot2, Model Evaluation, Plot (Graphics), Regression Analysis, Predictive Analytics, Predictive Modeling, Statistical Visualization, Data-Driven Decision-Making, Statistical Modeling, R Programming, Descriptive Statistics, R (Software), Exploratory Data Analysis, Descriptive Analytics, Statistical Analysis, Data Analysis

    ★ 4.2 (13) · Intermediate · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
  • C

    Coursera

    Create Your First Python Program From UST

    Skills you'll gain: Python Programming, Command-Line Interface, Programming Principles, Computer Programming, Scripting, Computer Programming Tools, Program Development, Development Environment

    ★ 4.6 (3.1K) · Beginner · Guided Project · Less Than 2 Hours

    Category: Free
    Free
    Category: Credit offered
    Credit offered
  • C

    Coursera

    Getting Started with Rstudio

    Skills you'll gain: Interactive Data Visualization, Plot (Graphics), R (Software), Hypertext Markup Language (HTML), Software Installation, Package and Software Management, R Programming, Integrated Development Environments, Statistical Programming

    ★ 4.6 (146) · Beginner · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered

What brings you to Coursera today?

  • C

    Coursera

    Prompt Engineering with GPT: Programming for Custom Content

    Skills you'll gain: Prompt Engineering, OpenAI API, Education Software and Technology, AI Integrations, OpenAI, Scripting, Python Programming, Application Programming Interface (API), Application Development, Machine Learning, Software Engineering

    ★ 4.4 (75) · Intermediate · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
  • C

    Coursera

    Create Interactive Dashboards with Streamlit and Python

    Skills you'll gain: Plotly, Dashboard, Plot (Graphics), Pandas (Python Package), Dashboard Creation, Data Manipulation, Interactive Data Visualization, Data Visualization Software, Data Visualization, Web Applications, Web Design and Development, Application Development, Data Presentation, Data Science, Python Programming

    ★ 4.7 (402) · Intermediate · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
  • C

    Coursera

    Mastering Data Analysis with Pandas

    Skills you'll gain: Pandas (Python Package), Data Analysis, Data Manipulation, Python Programming

    ★ 4.6 (181) · Beginner · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
  • C

    Coursera

    Tweet Emotion Recognition with TensorFlow

    Skills you'll gain: Recurrent Neural Networks (RNNs), Tensorflow, Model Optimization, Python Programming, Model Training, Natural Language Processing, Data Preprocessing, Applied Machine Learning, Artificial Neural Networks, Text Mining, Machine Learning Algorithms, Deep Learning, Classification Algorithms, Machine Learning

    ★ 4.5 (174) · Intermediate · Guided Project · Less Than 2 Hours

    Category: Free
    Free
    Category: Credit offered
    Credit offered
  • C

    Coursera

    Programming in C#: A comprehensive approach to C# Fundamentals

    Skills you'll gain: Microsoft Visual Studio, C# (Programming Language), Integrated Development Environments, Microsoft Development Tools, Program Development, Programming Principles, C and C++, Computer Programming, Command-Line Interface, Computational Logic

    ★ 4.4 (219) · Beginner · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
  • C

    Coursera

    Data Science Challenge

    Skills you'll gain: Applied Machine Learning, Jupyter, Machine Learning Algorithms, Model Evaluation, Machine Learning, Model Training, Predictive Modeling, Data Science, Python Programming, Predictive Analytics, Data Analysis

    ★ 4.6 (214) · Intermediate · Guided Project · Less Than 2 Hours

    Category: Credit offered
    Credit offered
1234…37

In summary, here are 10 of our most popular r programming courses

  • Getting Started with R: Coursera
  • Introduction to R: Basic R syntax: Coursera
  • Data Visualization using dplyr and ggplot2 in R: Coursera
  • Data Analysis in R: Predictive Analysis with Regression: Coursera
  • Create Your First Python Program From UST: Coursera
  • Getting Started with Rstudio: Coursera
  • Prompt Engineering with GPT: Programming for Custom Content: Coursera
  • Create Interactive Dashboards with Streamlit and Python: Coursera
  • Mastering Data Analysis with Pandas: Coursera
  • Tweet Emotion Recognition with TensorFlow: Coursera

Skills you can learn in Data Analysis

Analytics (85)
Big Data (64)
Python Programming (47)
Business Analytics (40)
R Programming (37)
Statistical Analysis (36)
Sql (33)
Data Model (29)
Data Mining (27)
Exploratory Data Analysis (26)
Data Modeling (21)
Data Manipulation (20)

Frequently Asked Questions about R Programming

R programming is a language and environment specifically designed for statistical computing and data analysis. It is widely used among statisticians, data analysts, and researchers for its powerful capabilities in data manipulation, statistical modeling, and visualization. The importance of R programming lies in its ability to handle complex data sets and perform advanced analyses, making it a valuable tool in various fields, including finance, healthcare, and academia.‎

With skills in R programming, you can pursue various job roles such as data analyst, statistician, data scientist, and research analyst. These positions often require a strong understanding of data manipulation and analysis, as well as the ability to communicate findings effectively. Industries such as technology, finance, and healthcare are particularly keen on professionals who can leverage R to derive insights from data.‎

To learn R programming effectively, you should focus on several key skills. These include understanding the basics of programming concepts, data structures, and functions in R. Additionally, familiarity with data visualization libraries like ggplot2, data manipulation packages like dplyr, and statistical modeling techniques will enhance your proficiency. Knowledge of the R ecosystem and how to use R for data analysis is also crucial.‎

Some of the best online courses for R programming include the Introduction to R Programming for Data Science and the Data Analysis with R Programming. These courses provide foundational knowledge and practical skills that can help you get started with R programming and apply it to real-world data analysis.‎

Yes. You can start learning r programming on Coursera for free in two ways:

  1. Preview the first module of many r programming courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in r programming, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn R programming, start by selecting a beginner-friendly course that covers the basics. Engage with interactive exercises and projects to apply what you learn. Utilize online resources, such as forums and documentation, to enhance your understanding. Consistent practice and real-world application of R will help solidify your skills and boost your confidence in using the language.‎

Typical topics covered in R programming courses include data types and structures, functions, control flow, data visualization, and statistical analysis. Advanced courses may explore topics like machine learning, data wrangling, and the use of R packages for specific applications. Understanding these topics will provide a comprehensive foundation for using R in various data-related tasks.‎

For training and upskilling employees in R programming, courses like the Microsoft R Programming for Everyone Professional Certificate and the Expressway to Data Science: R Programming and Tidyverse Specialization are excellent choices. These programs are designed to equip learners with practical skills that can be directly applied in the workplace.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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