𝐖𝐚𝐧𝐭 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐑 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 but Don’t Know Where to Start? This Course Is for You!
Course Details
📌 What makes this course worth it?
✅ No prior programming experience required
✅ Learn step-by-step, from basics to real-world agricultural data tasks
✅ Perfect for research, thesis work, and data-driven careers
📖 What will you learn?
- Fundamentals of R & RStudio setup
- Data structuring and transformation
- Statistical analysis
- Visualization tools
- R packages for agriculture-focused data analysis
- Applying AI tools to assist R code writing
- Real-time project experience with published article case studies
👩🎓 Who is this course for?
- Students or graduates from any background
- Researchers seeking to analyze agricultural or environmental data
- Data analysts aiming to expand into R-based workflows
- Anyone interested in building a career in programming and data science
Instructor
Md. Raihanul Islam
Course Instructor at SNK crop care
Md. Raihanul Islam is a Graduate Research Assistant at Gazipur Agricultural University (GAU), specializing in precision agriculture, crop physiology, remote sensing, and crop weather modeling.
He integrates UAV-based imaging, and geospatial analytics to study plant stress, yield responses, and environmental sustainability.
His works are published in international peer-reviewed journals, covering topics such as stress physiology, UAV phenotyping, urbanization metrics, and environmental pollution.
Raihanul is proficient in R, Python, Google Earth Engine, ArcGIS Pro, and QGIS, with strong experience in data analysis, visualization, and scientific interpretation.
He has collaborated with national research institutes (BRRI, CDB) and government projects involving multispectral UAV imaging and natural resource mapping.
Through this course initiative, he aims to train students and early-career researchers in data analytics, geospatial workflows, and scientific writing.
Syllabus
- Why R matters in agricultural research
- Overview of applications: crop data, soil analysis, climate datasets
- Understanding R’s ecosystem
- Step-by-step installation of R and RStudio
- Navigating the RStudio interface
- Setting up working directories and projects
- Data types (numeric, character, logical)
- Vectors, lists, and matrices
- Writing simple scripts and commands
- Data frames and factors
- Conditional statements and loops
- Functions and their applications
- Installing and loading packages
- Overview of tidyverse, ggplot2, dplyr, agricolae
- Package management best practices
- Importing datasets (CSV, Excel)
- Handling missing values
- Reshaping and merging datasets
- Using dplyr for filtering, grouping, summarizing
- Transforming agricultural datasets
- Creating clean tables and reports
- ANOVA test for crop trials
- LSD and Duncan’s multiple range test
- Practical examples with agricultural data
- Box plots for yield comparisons
- PCR and correlation matrix analysis
- Customizing plots with ggplot2
- Walkthrough of a research paper analyzed with R
- Replicating data analysis steps
- Understanding how R strengthens scientific communication
- Career paths: researcher, analyst, programmer
- Using AI tools to assist R code writing
- How R skills enhance employability in agriculture and beyond
- Hands-on project using real agricultural dataset
- Group presentations of findings
- Final Q&A and feedback session
What’s Inside This Course
- 8+ hours of video lessons – Step-by-step guidance from basics to intermediate R.
- Programming book in Bangla – Easy reference for learners in their native language.
- Quizzes & practice exercises – Reinforce learning and test your skills.
- Lifetime access – Learn at your own pace, revisit anytime.
- Beginner to intermediate coverage – No prior coding experience required.
- Exclusive resources – Sample datasets, code templates, and research toolkits.
- Course certificate – Showcase your achievement for academic or career growth.
- Hands-on agricultural datasets – Work with real crop, soil, and climate data.
- Published article walkthrough – Learn how R is used in actual research papers.
- Career guidance – Explore roles in research, data analysis, and agri-tech.
- AI-assisted coding – Discover how AI tools can help write and optimize R scripts.
- Community support – Access peer discussions, Q&A forums, and mentorship.
Real-time project – Apply your skills to a practical agricultural problem and present findings.
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