Introduction to R for microbial ecologists Course Overview

Last Updated : 04 Jun 2025

Introduction 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.

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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 Labs)	28,359 (INR)

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Schedule personalized sessions based upon your availability.

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Customized Training

Learning without limits. Create custom courses that fit your exact needs, from blended topics to brand-new content.

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Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.

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Immerse yourself in a focused learning environment, free from distractions, where you can sharpen your skills in popular global destinations.

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Target Audience for Introduction to R for microbial ecologists

This course equips microbial ecologists with the R programming skills needed for data analysis, visualization, and statistical modeling in their field.

Microbial Ecologists
Bioinformaticians
Biostatisticians
Environmental Scientists
Data Analysts in Biology
Computational Biologists
Ecological Modelers
PhD Students in Microbiology or Ecology
Postdoctoral Researchers in Life Sciences
Academic Researchers focusing on Microbial Ecology
Laboratory Technicians in Microbiology Labs
Government Scientists working in Environmental Regulation
Biotechnology Professionals analyzing microbial data
Healthcare Professionals in Epidemiology and Infection Control
Scientific Programmers in the field of Microbial Research

Learning Objectives - What you will Learn in this Introduction to R for microbial ecologists?

Introduction to Learning Outcomes and Concepts Covered:

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.

Learning Objectives and Outcomes:

Understand the basic R syntax and how to manipulate data structures such as vectors, matrices, lists, data frames, and factors.
Develop the ability to perform data subsetting, sorting, and applying descriptive statistics for exploratory data analysis.
Gain proficiency in data visualization using R's ggplot2 package to create various plots such as bar plots, histograms, and heat maps tailored to microbial data.
Learn to test for normality, conduct correlation and regression analysis, and apply ANOVA and linear models for statistical analysis in R.
Acquire skills to perform multivariate analysis, time series analysis, survival analysis, and utilize machine learning algorithms within R.
Explore and analyze microbial data, including high-throughput sequencing, metagenomic, and microbial community data.
Master techniques for microbial data manipulation, including data wrangling, cleaning, integration, and aggregation.
Learn to model microbial communities using statistical and machine learning approaches such as random forests, support vector machines, and neural networks.
Gain insights into advanced topics such as microbial diversity analysis, modeling microbial dynamics, and working with various types of microbial 'omics data.
Develop the ability to handle microbial ecological, evolutionary, and interaction data, facilitating comprehensive understanding and interpretation of microbial datasets.