Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
2 Hour(s) 37 Minute(s)
Language
English
Taught by
Mawunyo Simon Pierre KITEGI
Rating
Course Overview
Master Agriculture Data Analysis with R: Tune In Now for Live Coding Exercises!
Get The Secrets to use R and RStudio to Analyse your Agriculture Data with Practical Coding Exercises
The course is designed to provide students with a comprehensive introduction to data collection, analysis, and reproducible report preparation using R and R studio. The course focuses on the context of environmental and agricultural science, as well as environmental and agricultural economics, to provide relevant examples to students.
Throughout the course, students learn to identify the appropriate statistical techniques for different types of data and how to obtain and interpret results using the R software platform. The course covers various statistical methods such as ANOVA, linear regression, generalized linear regression, and non-parametric methods. Online lectures are used to explain and illustrate these methods, and practical computer-based exercises are provided to help students develop their knowledge and understanding of each approach.
In addition to statistical methods, the course also introduces basic programming concepts that allow R to be used for automating repetitive data management and analysis tasks. Students are also exposed to the advanced graphics capacity of R and learn about the workflow for reproducible report generation.
Upon completion of the course, students will have the knowledge and skills necessary to undertake data analysis at a standard that meets most workplace demands using R. This course provides a strong foundation for further study and application of data analysis techniques, making it an essential course for students pursuing careers in environmental and agricultural sciences or related fields.
Overall, the course aims to equip students with practical skills and knowledge for data analysis and report generation in the context of environmental and agricultural sciences, which will help them become better-prepared professionals in their future careers.
Course Content
- 10 section(s)
- 44 lecture(s)
- Section 1 Using R for Agricultural Data: First important skills
- Section 2 R Programming: The basics
- Section 3 R Programing: Descriptive Statistics on Agricultural Experimental Data
- Section 4 R Programming: Simple Correlation [Response of winter wheat to saflufenacil]
- Section 5 R Programming: Compare yields with Student t-test !
- Section 6 R Programming: Analyse of Variance (ANOVA)
- Section 7 R Programming: Linear Regression in R
- Section 8 R Programming: Analysis of Covariance using R
- Section 9 Summary
- Section 10 R Programming: R packages for Agricultural Research
What You’ll Learn
- R Programming
- Agriculture statistics using R
- Reporting using R
- Data vizualisation in R
Skills covered in this course
Reviews
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CChandrayee Chakraborty
Very good experience
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KKoffi Aquilas Agbevohia
oui c'est un cours simple à comprendre et l'enseignant fait très bien sont travail, mais en ce qui concerne les exercices qu'il donne es ce qu'il y a une possibilité d'avoir les réponses de ces exercices pour pouvoir vérifier si on l'a bien fait
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PPranay Kumar Kadari
The course offers a strong foundation with practical examples, but content organization and theoretical explanations could be more structured for clarity. Overall, it's an informative and valuable learning experience as a beginner to R programming.
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UUsama Bin Tariq
Course content is good and very helpful if you are at the beginner level. But the proper explanation for a beginner guy like me who have a background in biological science and not having any experience in programming is poor. Overall it increases my confidence and thank you for the creation of this course. You should have basic understanding of R environment to complete this course. The content is very very helpful
