Unable to find what you're searching for?
We're here to help you find itIntroduction to R for microbial ecologists Course Overview
The "Introduction to R for Microbial Ecologists" course is a comprehensive program designed specifically for scientists and researchers in the field of microbiology who wish to harness the power of R for data analysis and visualization. Microbial ecologists will learn to master R syntax and various data structures through a series of progressively challenging modules, starting with basic R programming concepts and advancing to sophisticated statistical analysis and modeling of microbial communities.
Learners will gain proficiency in manipulating, exploring, and visualizing complex microbial data using R's powerful packages and functions. The course covers essential topics such as ggplot2 for data visualization, descriptive statistics for data exploration, and various machine learning algorithms for in-depth data analysis. By the end of the course, participants will be equipped with the skills to handle high-throughput sequencing data, metagenomic data, and perform microbial community analysis, thus enhancing their research capabilities and contributing to their professional growth in microbial ecology.
Purchase This Course
USD
View Fees Breakdown
Flexi Video | 16,449 |
Official E-coursebook | |
Exam Voucher (optional) | |
Hands-On-Labs2 | 4,159 |
+ GST 18% | 4,259 |
Total Fees (without exam & Labs) |
22,359 (INR) |
Total Fees (with exam & Labs) |
28,359 (INR) |
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
This course equips microbial ecologists with the R programming skills needed for data analysis, visualization, and statistical modeling in their field.
The "Introduction to R for Microbial Ecologists" course is designed to equip students with the skills to analyze and visualize microbial data using R, focusing on statistical analysis, data manipulation, and modeling microbial communities.