| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 1 Month |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹2499 / $59 |
| Tools | R RStudio ggplot2 dplyr tidyverse Shiny caret knitr |
About the R Programming: Basic to Advanced Course
The beginner's R programming course is designed for those new to R and aims to provide a solid foundation in R programming and data analysis. Participants will learn the basics of the language and data analysis techniques, and gain hands-on experience working with real-world data sets. The workshop will provide participants with the skills and knowledge they need to continue learning R programming on their own.
The advanced R programming workshop is designed for those who already have some experience with R and aims to build on the concepts learned in the beginner's workshop. Participants will learn advanced data manipulation and visualization techniques, advanced statistical analyses, handling large datasets, and reproducible data analysis workflows. The workshop will provide participants with the skills and knowledge they need to perform more complex data analysis tasks and apply R in specific domains such as bioinformatics, text mining, and machine learning.
Program Highlights
• Comprehensive coverage of R Programming from fundamentals to advanced applications
• Hands-on projects and real-world case studies in R Programming
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in R Programming
• Career-oriented training for academic and professional growth in R Programming
Course Curriculum
Module 1: Introduction to R Programming
- Overview and historical evolution of R Programming
- Key terminology, definitions, and core concepts in R Programming
- Current industry landscape, trends, and career opportunities
- Setting up the learning environment and essential tools
Module 2: Fundamentals and Theoretical Foundations
- Core principles and scientific/theoretical underpinnings of R Programming
- Mathematical and analytical frameworks relevant to R Programming
- Comparative analysis of major approaches and methodologies
- Understanding key standards, guidelines, and best practices
Module 3: Basic to Advanced Course
- Core concepts and techniques in Basic to Advanced Course
- Practical implementation and hands-on exercises
- Integration of Basic to Advanced Course with R Programming workflows
- Case study: Real-world application of Basic to Advanced Course
Module 4: Data Wrangling
- Introduction to Data Wrangling concepts and methodologies
- Step-by-step practical implementation of Data Wrangling techniques
- Tools and platforms commonly used for Data Wrangling
- Troubleshooting, optimization, and best practices
Module 5: Statistical Modeling
- Introduction to Statistical Modeling concepts and methodologies
- Step-by-step practical implementation of Statistical Modeling techniques
- Tools and platforms commonly used for Statistical Modeling
- Troubleshooting, optimization, and best practices
Module 6: Advanced Topics and Emerging Trends in R Programming
- Cutting-edge research and innovations in R Programming
- Integration with AI, automation, and modern technologies
- Industry case studies and real-world problem solving
- Future directions and career pathways in R Programming
Module 7: Capstone Project and Assessment
- End-to-end project implementation using R Programming skills
- Peer review, collaborative exercises, and expert feedback
- Portfolio-ready project documentation and presentation
- Final assessment and course completion evaluation
Tools, Techniques, or Platforms Covered
R RStudio ggplot2 dplyr tidyverse Shiny caret knitr
Real-World Applications
- Apply R Programming skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical R Programming competencies
- Solve industry-relevant problems using R Programming methodologies and tools
- Contribute to open-source projects and collaborative research in R Programming
- Prepare for competitive examinations, interviews, and professional certifications in R Programming
Who Should Attend & Prerequisites
- Students pursuing degrees in R Programming, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into R Programming roles
- Researchers and academicians looking to adopt modern techniques in R Programming
- Entrepreneurs, freelancers, and self-learners interested in practical R Programming knowledge
Certification
