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R Programming for Biologists: Beginners Level

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R Programming for Biologists: Beginners Level

USD $59.00 USD $249.00

Aim: The aim of the workshop is to empower biologists with essential skills in R programming, fostering a comprehensive understanding of its applications in biological research. This workshop aims to demystify the complexities of R, providing participants, from beginners to intermediate users, with a solid foundation in utilizing R for data analysis in the biological sciences. Through a combination of theoretical knowledge and hands-on practical sessions, attendees will gain proficiency in data manipulation, statistical analysis, and visualization, enabling them to harness the full potential of R as a powerful tool for extracting meaningful insights from biological datasets. By the end of the workshop, participants will be equipped with the confidence and skills necessary to integrate R seamlessly into their research workflows, ultimately enhancing the efficiency and depth of their biological analyses.

Aim

R Programming for Biologists: Beginners Level teaches R basics for biological data handling, simple statistics, and clear plots. Learn core coding, work with lab datasets (CSV/TSV), and create reproducible outputs for faster analysis.

Program Objectives

R Setup: RStudio, projects, scripts, packages, help.
Core R: objects, vectors, data frames, indexing, functions.
Data Handling: import, clean, filter, summarize, export.
Visualization: ggplot2 plots for experiments.
Basic Statistics: descriptive stats + simple tests (intro).
Good Practice: reproducible workflow, clean code, documentation.
Mini Project: complete a small end-to-end analysis.

Program Structure

Module 1: Getting Started with R

Install R/RStudio; create projects; organize folders.
Console vs script; working directory; help system.
Install/load packages; tidyverse overview.
First task: load data and compute summaries.

Module 2: Fundamentals (Objects, Vectors, Indexing)

Data types: numeric, character, logical, factor.
Vectors + indexing; basic calculations.
Missing values (NA): detect and handle.
Key functions + simple custom function.

Module 3: Data Frames for Lab Data

Data frames/tibbles; inspect and summarize.
Import/export: CSV/TSV; common format issues.
Clean: rename columns, fix types, remove duplicates.
Filter/sort: control vs treatment examples.

Module 4: Wrangling with dplyr

select, filter, mutate, arrange, summarise, group_by.
Group summaries: mean/SD by condition, replicates, time points.
Joins (intro): combine metadata + measurements.
Reshape (intro): wide vs long for plotting.

Module 5: Visualization with ggplot2

Bar, line, scatter, boxplot, histogram.
Biology plots: growth curves, QC plots, distributions.
Clarity: labels, scales, legends, honest visuals.
Export plots for reports/slides.

Module 6: Basic Statistics (Intro)

Mean/median, SD, IQR; distribution checks (basic).
t-test and chi-square concepts (intro).
p-values + effect size thinking (intro); common mistakes.
Simple reporting of results.

Module 7: Reproducible Reporting

Save clean tables/plots to an outputs folder.
RMarkdown/Quarto basics (intro).
Document assumptions with comments + README.
Reproducibility checklist.

Final Mini Project

Analyze a provided biology dataset (growth/enzyme/qPCR Ct table/non-diagnostic/survey).
Workflow: import → clean → summarize → plot → optional simple test.
Deliverables: script/notebook + results table + 2–3 plots + short report.

Participant Eligibility

UG/PG students in Biotechnology, Microbiology, Genetics, Life Sciences, Bioinformatics
Researchers/lab members handling experimental datasets
Beginners (no coding required)

Program Outcomes

Write basic R scripts and use RStudio confidently.
Import, clean, and summarize biology datasets.
Create clear plots for experiments.
Run and interpret simple stats (intro level).
Deliver a reproducible mini project.

Program Deliverables

e-LMS Access: lessons, datasets, templates.
Starter Pack: setup guide, import templates, dplyr cheatsheet, ggplot scripts.
Practice Tasks: exercises with solutions.
Project Support: guidance for mini project completion.
Assessment: certification after assignments + mini project.
e-Certification and e-Marksheet: digital credentials on completion.

Future Career Prospects

Research Data Assistant (Life Sciences)
Junior Bioinformatics/Data Analysis Trainee
Lab Data Analyst (Entry-level)
Biostatistics Assistant (Entry-level)

Job Opportunities

Labs & Universities: data cleaning, plotting, reporting support.
Biotech/Pharma: basic analytics and QC summaries.
CROs/Core Facilities: documentation and results reporting support.
Startups: experiment data handling and visualization.

Category	E-LMS, E-LMS+Video, E-LMS+Video+Live Lectures

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Artificial Intelligence(ai)

R Programming for Biologists: Beginners Level

Aim

Program Objectives