Installation and configuration of R and RStudio along with execution of basic commands for understanding the R programming workflow and environment.

Understanding different data types and variable creation in R for storing, managing, and processing biological and experimental datasets.

Understanding different data types and variable creation in R for storing, managing, and processing biological and experimental datasets.

Introduction to vectors and matrices in R for organizing, storing, and manipulating biological and experimental data efficiently.

Practice-based exercise on creating, modifying, and analyzing biological datasets using matrix operations in R.

Understanding lists and data frames in R for organizing, managing, and analyzing complex biological and experimental datasets efficiently.

Hands-on practice in creating structured biological datasets using lists and data frames in R for further analysis and interpretation.

Introduction to conditional statements and looping structures in R for automating repetitive biological computations and data analysis tasks.

Hands-on exercise to generate and analyze random DNA sequences using loops and conditional statements in R programming.

Introduction to user-defined functions and file handling techniques in R for automating biological analyses and managing experimental datasets efficiently.

Hands-on practice in importing, exporting, and managing biological datasets using different file formats in R programming.

Introduction to statistical methods in R for analyzing biological datasets, performing hypothesis testing, and interpreting experimental results.

Hands-on exercise to perform t-test analysis and interpret statistical results from biological and experimental datasets using R.

Comprehensive practical assessment designed to evaluate participants’ understanding of R programming concepts, biological data handling, statistical analysis, visualization techniques, and problem-solving abilities through hands-on exercises, real biological dataset analysis, coding tasks, and mini project submission. The evaluation emphasizes analytical thinking, computational workflow development, interpretation of results, and the practical application of R in biological and bioinformatics research.

Introduction to the Dplyr package for efficient biological data manipulation, filtering, sorting, summarization, and transformation of large experimental datasets in R.

Hands-on exercise focused on filtering, organizing, summarizing, and interpreting human genome datasets using the Dplyr package for biological data analysis in R.

Introduction to data visualization using ggplot2 for creating publication-quality graphical representations of biological and experimental datasets in R.

Hands-on exercise focused on generating and interpreting scientific visualizations of biological datasets using ggplot2, including scatter plots, bar graphs, and comparative data representation techniques in R.

Introduction to advanced data visualization techniques in R using ggplot2 and RColorBrewer for generating publication-quality boxplots, violin plots, line plots, and comparative biological data representations.

Hands-on exercise focused on creating comparative biological visualizations using advanced plotting techniques in ggplot2, including boxplots, violin plots, and multi-variable graphical representations for effective data interpretation and scientific reporting.

Introduction to the Seqinr package for analyzing DNA and protein sequences, calculating sequence composition, and handling FASTA and MultiFASTA biological datasets in R.

Hands-on exercise focused on importing, analyzing, and interpreting FASTA and MultiFASTA sequence datasets using Seqinr for nucleotide composition, sequence properties, and biological insights in R.

Introduction to the Bioconductor platform for analyzing high-throughput biological datasets, including genomic and Next-Generation Sequencing (NGS) data, using specialized bioinformatics packages in R.

Hands-on exercise focused on importing, processing, and interpreting FASTQ-based Next-Generation Sequencing (NGS) datasets using Bioconductor packages for sequence quality assessment, read analysis, and biological data interpretation in R.

Introduction to biological data reshaping techniques in R using packages such as Dplyr and Tidyr for transforming, organizing, and preparing complex datasets into analysis-ready formats.

Hands-on exercise focused on transforming and restructuring biological datasets using Dplyr and Tidyr packages for efficient data organization, cleaning, and preparation for downstream statistical analysis and visualization in R.

Introduction to advanced scientific visualization and reporting techniques in R for generating publication-quality figures, graphical summaries, and analytical reports for biological data interpretation and research communication.

Introduction to scientific review article writing involving literature survey, biological data interpretation, statistical analysis, visualization integration, citation management, and preparation of a structured mini review article based on computational and bioinformatics analyses performed during the course.

Comprehensive final evaluation involving presentation of the mini review article, biological data analysis workflow, statistical interpretation, sequence analysis findings, and scientific visualizations developed during the course. Participants will be assessed on coding proficiency, analytical reasoning, data interpretation skills, scientific communication, problem-solving approach, and the effective integration of computational biology concepts into research-oriented applications.