Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Genomics: Basics is an accessible entry point into how machine learning is applied to the genome. You learn how genomic data is represented and the problems AI helps solve: interpreting variants, predicting gene function and regulatory elements, and finding patterns across large sequence datasets. The course introduces the key methods at a foundational level and connects them to real questions in research and precision medicine, without assuming prior computational depth. You finish with a solid conceptual grounding in AI-driven genomics. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This foundational course covers AI in genomics — how machine learning analyses DNA and sequence data for variant interpretation, gene prediction and genomic insight.
📋 Course Objectives
1. Understand how genomic data is represented.
2. See where AI helps in variant interpretation.
3. Explain gene and regulatory-element prediction.
4. Recognise pattern-finding across sequence data.
5. Connect methods to research and precision medicine.
👥 Who Should Enroll?
• Life scientists new to computational genomics
• Students entering bioinformatics
• Research and clinical-lab staff
• Anyone curious about AI in genetics
🚀 Key Learning Outcomes
• A foundational grasp of AI in genomics.
• The ability to follow genomic-AI methods.
• A springboard to deeper bioinformatics.
• 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
Introduction to AI and Genomics
What is Artificial Intelligence? • Basics of Genomics and DNA • Role of AI in Genomic Research • Applications in Healthcare and Biotechnology
Understanding Genomic Data
DNA, Genes, and Genetic Variations • Introduction to Sequencing Data • Gene Expression and Biological Information • Importance of Data Quality in Genomics
AI Concepts in Genomics
Introduction to Machine Learning Basics • Pattern Recognition in Genomic Data • AI for Variant and Disease Prediction • Basic Predictive Analysis Concepts
Applications of AI in Genomics
AI in Disease Research and Diagnostics • Personalized Medicine and Precision Healthcare • Drug Discovery and Biomarker Identification • Genomics in Biotechnology and Agriculture
Future Scope and Learning Path
Emerging Trends in AI-Driven Genomics • Big Data and Cloud Genomics Concepts • Career Opportunities in AI and Genomics • Mini Learning Activity / Concept-Based Practice
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Artificial Intelligence |
| Covered Tool / Platform | Genomics |
| Covered Tool / Platform | DNA Analysis |
| Covered Tool / Platform | Machine Learning Basics |
| Covered Tool / Platform | Genetic Data Interpretation |
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