Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This 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. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
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.
📋 Course Objectives
1. Apply biotechnology methods to authentic research and industry problems.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A portfolio-grade biotechnology deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
The R Language
• Vectors, lists, factors and data frames, and R's vectorised idiom
• Environments, scoping and the common surprises they cause
• Projects, RStudio workflow and reproducible setup with renv
Wrangling With the Tidyverse
• dplyr verbs and pipeline-style transformation
• tidyr reshaping and the tidy data principle
• Joins, grouping and window operations on real datasets
ggplot2 in Depth
• Grammar of graphics: aesthetics, geoms, scales and facets
• Publication-quality theming and export at correct dimensions
• Choosing an encoding that answers the question honestly
Modelling in R
• Linear and generalised linear models and reading model output
• Mixed models for grouped and repeated-measures data
• Diagnostics, assumptions and what to do when they fail
Programming and Reporting
• Writing functions, handling errors and testing with testthat
• purrr for functional iteration and avoiding copy-paste analysis
• R Markdown and Quarto for reports that regenerate from data
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | RStudio |
| Covered Tool / Platform | ggplot2 |
| Covered Tool / Platform | dplyr |
| Covered Tool / Platform | tidyverse |
| Covered Tool / Platform | Shiny |
| Covered Tool / Platform | caret |
| Covered Tool / Platform | knitr |
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