Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Omics data has revolutionized biological research by enabling the large-scale study of genes, proteins, metabolites, and their complex interactions. However, handling and interpreting this vast amount of data can be daunting. AI and machine learning offer powerful approaches to uncover hidden patterns, make predictions, and gain insights that were previously unattainable using traditional methods. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Omics data has revolutionized biological research by enabling the large-scale study of genes, proteins, metabolites, and their complex interactions. However, handling and interpreting this vast amount of data can be daunting. AI and machine learning offer powerful approaches to uncover hidden patterns, make predictions, and gain insights that were previously unattainable using traditional methods.
📋 Course Objectives
1. Apply bioinformatics methods to authentic research and industry problems.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in bioinformatics
• R&D engineers and working professionals applying bioinformatics in industry
• Academics and educators building research or teaching capacity in bioinformatics
🚀 Key Learning Outcomes
• A portfolio-grade bioinformatics deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Framing the Biological Question
• Matching omics modality to the question being asked
• Experimental design, replication and power in omics studies
• Confounding by batch, sex, age and collection site
From Raw Data to Expression Matrix
• Quality control, trimming and alignment or pseudoalignment
• Normalisation choices and their downstream consequences
• Batch correction methods and the risk of removing real signal
Differential and Enrichment Analysis
• Differential expression with appropriate dispersion modelling
• Multiple testing correction and effect-size reporting
• Pathway and gene-set enrichment, and the interpretive traps in it
Multi-Omic and Network Analysis
• Integrating transcriptomic, proteomic and metabolomic layers
• Co-expression and regulatory network inference, and its weak identifiability
• Prioritising candidates for experimental follow-up
Turning Analysis Into a Claim
• Independent cohort validation and reproducibility expectations
• Designing the wet-lab experiment that would falsify the finding
• Reporting standards and data deposition for omics publications
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
Frequently Asked Questions
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