Free R Programming Courses & Certifications Online

Particular

Details

Course Level

Beginner to Advanced

Duration

2-6 months (depending on the level)

Eligibility

Basic programming knowledge preferred

Fees

Varies from free to paid courses (₹5,000 - ₹50,000)

Top Institutes

IIT Madras, IIM Calcutta, Coursera, edX, Udemy

Median Salary

₹6.5 LPA (Entry-level), ₹12 LPA (Experienced)

Top Recruiters

Amazon, Google, Microsoft, IBM, Accenture

Job Positions

Data Analyst, Data Scientist, Statistician, Business Analyst

Reason

Details

Data Analysis and Visualization

R is designed for data analysis, providing numerous packages like dplyr and ggplot2 for data manipulation and visualization.

Statistical Computing

R is powerful for statistical computing, with built-in functions and packages like lme4, survival, and caret.

Machine Learning

R supports machine learning through packages like randomForest, xgboost, caret, and h2o for various algorithms.

Open Source

R is open-source and free, supported by a large community of developers and researchers who contribute packages and updates.

Comprehensive Package Ecosystem

The Comprehensive R Archive Network (CRAN) hosts over 18,000 packages, covering a wide range of statistical and graphical techniques.

Reproducible Research

Tools like RMarkdown, knitr, and Sweave integrate code, output, and documentation, promoting reproducible research.

Cross-Platform Compatibility

R is compatible with Windows, MacOS, and Linux, ensuring that scripts and applications can run on any operating system.

Integration with Other Languages

R can interface with Python, C++, Java, and other languages using packages like reticulate and Rcpp, offering flexibility in development.

Strong Community Support

The R community provides extensive support through forums like Stack Overflow, user groups, and annual conferences like useR! and RStudio Conference.

Career Opportunities

R skills are in demand across various industries, including finance, healthcare, academia, and tech, with roles such as Data Analyst, Data Scientist, and Statistician.

Educational Resources

There are abundant learning resources for R, including MOOCs, online tutorials, textbooks, and courses on platforms like Coursera, edX, and DataCamp.

Data Wrangling

R excels in data wrangling with packages like dplyr for data manipulation, tidyr for data tidying, and stringr for string operations.

Advanced Graphics

R's ggplot2 package is renowned for creating sophisticated and customizable visualizations, from simple plots to complex multi-panel charts.

Big Data Handling

R can handle large datasets using packages like data.table and can integrate with big data technologies like Hadoop and Spark through packages like sparklyr.

Automation

R allows automation of repetitive tasks in data analysis and reporting, with scripting capabilities and tools like Shiny for interactive web applications.

Stage

Goals and Actions

Resources

1. Introduction

Understand the basics of R: Learn what R is, its history, and its applications in data analysis and statistical computing.

1. R Project website: www.r-project.org
2. Introduction to R Programming for Data Science

2. Installation

Set up R and RStudio: Install R from CRAN, install RStudio as an IDE for R, and familiarize yourself with the RStudio interface.

1. CRAN website - www.cran.r-project.org

3. Basic Syntax

Learn basic R syntax and operations: Understand variables, data types, basic arithmetic operations, and writing simple functions in R.

1. Data Analysis With R Programming by Coursera
2. Data Science: R Basics by edX

4. Data Structures

Understand R data structures: Learn about vectors, matrices, lists, and data frames, and practice manipulating these structures.

5. Data Manipulation

Manipulate and clean data: Use dplyr for data manipulation, tidyr for data tidying, and practice data cleaning and transformation tasks.

1. Data Manipulation Online Courses and Certifications

6. Data Visualization

Create visualizations: Learn ggplot2 for creating various types of plots and visualizations, and practice creating and customizing plots.

1. Data Visualization in R with ggplot2 by Coursera
2. Data Visualization with R by Great Learning
3. Complete Data Wrangling & Data Visualization in R by Udemy

7. Statistical Analysis

Perform statistical analyses: Conduct basic statistical tests (t-tests, chi-squared tests), and learn linear and logistic regression techniques using R.

1. Statistical Analysis Course by Great Learning
2. Statistical Analysis with R Programming for Public Health Specialization

8. Machine Learning

Implement machine learning models: Learn basic ML algorithms (decision trees, random forests) and use the caret package for building and evaluating models.

1. Top Online Machine Learning Courses & Certifications in 2024
2. Introduction to Machine Learning with R by SimpliLearn
3. Machine Learning using Python and R Training
4. R and Machine Learning Fundamentals by TCSiON

9. Advanced Topics

Explore advanced R topics: Study time series analysis with forecast, text mining with tm and text2vec, and other specialized areas as per interest.

1. Data Science Certification Course Training
2. Introduction to Probability and Data with R
3. Introduction to R Software Conducted by IIT Alumni

10. Big Data Handling

Handle large datasets: Use data.table for efficient data handling, and integrate with big data tools like Hadoop and Spark through sparklyr.

1. Big Data Online Courses & Certifications
1. Big Data Analytics Courses by Coursera
2. Skillsoft courses on Big Data Analytics
3. Future Learning Courses on Big Data Analytics
4. Big Data Analytics Certificate Course by Great Learning 

11. Reproducible Research

Create reproducible research workflows: Use RMarkdown and knitr to combine code, output, and documentation in dynamic reports and presentations.

12. Project Work

Apply knowledge in real projects: Work on personal or open-source projects, and participate in Kaggle competitions to apply and refine your R skills.

1. Kaggle: www.kaggle.com
2. GitHub: www.github.com
3. Open-source R projects: ropensci.org

13. Community Engagement

Engage with the R community: Join R user groups and forums, participate in discussions, and attend R-related conferences and webinars.

1. R-Ladies: www.rladies.org
2. useR! conference: events.linuxfoundation.org/user/

14. Continuous Learning

Keep up-to-date with R developments: Follow R blogs and news, and take advanced and specialized courses to stay current with the latest trends and tools in R programming.

Check Advanced Courses on:

1. R Programming Online Courses on Coursera
2. Online R Programming Courses on Udemy
3. Great Learning R Programming Courses
4. Data Camp R Language Courses 

Course Name

Provider

Description

Duration

R Programming

Coursera (Johns Hopkins University)

Comprehensive introduction to R, covering data manipulation, visualization, and programming concepts

4 weeks

Statistics and R

edX (Harvard University)

Covers statistical concepts and their implementation in R, including data analysis, regression, and machine learning

8 weeks

Data Scientist with R

Udemy

End-to-end data science curriculum using R, from data manipulation to modeling and deployment

20 hours

R Programming for Data Science

DataCamp

Interactive course focused on using R for data science tasks, including data wrangling, visualization, and modeling

Self-paced

R for Data Science

Pluralsight

Practical R skills for data analysis, covering dplyr, ggplot2, and other popular packages

8 hours

R Programming: Advanced Analytics In R

Coursera (University of Michigan)

Advanced R programming concepts, including machine learning, text mining, and building data products

4 weeks

Data Analysis and Visualization Using R

edX (Microsoft)

Covers data analysis, visualization, and reporting using R and Power BI

6 weeks

R Programming: Master the Basics

Udemy

Beginner-friendly course covering R syntax, data structures, and basic programming concepts

6 hours

R Programming for Business Analytics

Coursera (University of Pennsylvania)

Application of R for business analytics, including data manipulation, modeling, and visualization

4 weeks

Interactive Data Visualization with R

DataCamp

Focuses on creating interactive and web-based visualizations using R packages like ggplot2, plotly, and shiny

Self-paced

Job Role

Skills Required

Salary (in Indian Currency)

Data Analyst

R, SQL, Excel, Data Visualization, Statistical Analysis

₹6.4 LPA

Data Scientist

R, Python, Machine Learning, Statistical Modeling, Big Data Tools

₹14.4 LPA

Statistician

R, Statistical Modeling, Hypothesis Testing, Experimental Design

₹5.8 LPA

Business Analyst

R, SQL, Data Analysis, Reporting, Business Intelligence

₹9.6 LPA

Quantitative Analyst

R, Financial Modeling, Risk Analysis, Portfolio Management

₹23 LPA

Marketing Analyst

R, Marketing Analytics, Customer Segmentation, A/B Testing

₹11.9 LPA

Research Analyst

R, Statistical Analysis, Data Visualization, Reporting

₹4.8 LPA

Bioinformatician

R, Bioinformatics Tools, Genomics, Computational Biology

₹9.7 LPA

Data Engineer

R, Python, SQL, Big Data Tools (Hadoop, Spark), Data Pipelines

₹10.7 LPA

Machine Learning Engineer

R, Python, Machine Learning Algorithms, Deep Learning

₹10.2 LPA

Concepts

Description

Data Types and Structures

Vectors, Matrices, Lists, Data Frames, Factors - building blocks for organizing and storing data in R

Data Manipulation

Subsetting, Merging, Reshaping data - techniques for selecting, combining, and transforming data

Control Structures

Conditional Statements (if-else), Loops (for, while) - for controlling the flow of execution

Functions

Writing and Using Functions - building blocks for modular programming and code reusability

Data Visualization

Base Plots, ggplot2 - creating static and interactive visualizations for data exploration and communication

Statistical Analysis

Descriptive Statistics, Hypothesis Testing, Regression - performing various statistical analyses on data

Data Import/Export

Reading and Writing Data from/to various formats (CSV, Excel, databases, etc.) - facilitating data exchange

Package Management

Installing, Loading, and Managing Packages - leveraging R's vast package ecosystem for extended functionality

Programming Paradigms

Functional Programming, Object-Oriented Programming - different programming styles in R

Debugging

Techniques for identifying and fixing errors in R code

Reproducible Research

R Markdown, Shiny - tools for creating dynamic reports and interactive web applications

Module

Topics

Introduction to R

R Basics, RStudio, Data Structures (Vectors, Matrices, Lists, Data Frames)

Data Manipulation

Subsetting, Merging, Reshaping Data, dplyr package

Data Visualization

Base Plots, ggplot2, Interactive Plots (plotly, shiny)

Statistics with R

Descriptive Statistics, Hypothesis Testing, Regression Analysis

Programming in R

Control Structures, Functions, Debugging

Data Import/Export

Reading and Writing Data (CSV, Excel, databases)

Advanced Data Manipulation

Tidyverse, data.table

Machine Learning

Introduction to Machine Learning, Supervised Learning (caret, randomForest), Unsupervised Learning (clustering)

Time Series Analysis

Time Series Data, Forecasting, Time Series Models (ARIMA, exponential smoothing)

Text Mining

Text Data Manipulation, Sentiment Analysis, Topic Modeling

Reproducible Research

R Markdown, Shiny Web Applications

Projects and Case Studies

Real-world Applications and Projects in various domains (e.g., finance, marketing, bioinformatics)

Application

Description

Healthcare and Bioinformatics

R is used for analyzing genomic data, clinical trial data, and bioinformatics. It helps in identifying genetic markers and analyzing patient data for better treatment plans.

Financial Analytics

R is used in finance for risk management, portfolio optimization, and fraud detection. Financial institutions use R to analyze large datasets and create predictive models.

E-commerce and Retail

R helps in customer segmentation, sentiment analysis, and sales forecasting. Retailers use R to analyze customer behavior and optimize their marketing strategies.

Social Media Analytics

R is used to analyze social media data to understand trends, sentiment, and user behavior. Companies use R to track brand sentiment and measure the impact of campaigns.

Environmental Science

R is employed in environmental science for analyzing climate data, air quality monitoring, and ecological modeling. It aids in understanding and predicting environmental changes.