This workshop aims to train participants in using Python and R for practical bioinformatics analysis across genomics, transcriptomics, and proteomics datasets. It focuses on data handling, preprocessing, visualization, and basic statistical and machine-learning workflows for high-throughput omics data. Through hands-on sessions, participants will learn how to work with FASTA/FASTQ, count matrices, differential expression data, and proteomics tables. The goal is to build confidence in using open-source tools and scripting to solve real biological questions.

• Use Python and R to import, clean, and explore genomics, transcriptomics, and proteomics datasets.
• Work with common bioinformatics file formats (FASTA, FASTQ, VCF, count matrices, expression tables).
• Perform basic statistical analysis and visualization (PCA, clustering, heatmaps, boxplots, volcano plots).
• Implement simple pipelines for differential gene/protein expression and functional interpretation.
• Build reproducible scripts and notebooks that can be adapted to their own datasets and projects.

• Undergraduate/postgraduate degree in Microbiology, Biotechnology, Bioinformatics, Computational Biology, Genomics, or related fields.
• Professionals in healthcare, pharma, diagnostics, biotech, or omics-based research labs.
• Data scientists and AI/ML engineers interested in applying coding and analytics to biological and omics datasets.
• Individuals with a keen interest in the convergence of life sciences, coding, and data-driven biological research.

• Gain practical experience using Python and R for omics data analysis.
• Learn to handle real-world genomics, transcriptomics, and proteomics datasets.
• Be able to run basic differential expression and exploratory analyses.
• Produce publication-ready plots and summaries for omics data.
• Develop reusable scripts/notebooks that can be adapted for future research projects.