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HEPro PGP in BioTech, Genetic Engineering, and Artificial Intelligence

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Executive Summary

The program is structured in five progressive phases spanning 15 months (60 weeks), designed for early-stage professionals and advanced life science graduates seeking market-ready competencies in biotechnology, genetic engineering, and AI. The curriculum is 100% project-based with industry partnerships, mentorship, and practical skill development integrated throughout. Each phase builds on previous learning, culminating in a comprehensive capstone project that demonstrates integration of all competencies

Immediate Positions (0-2 years post-program):

  • Genetic Engineer (CRISPR therapy development)

  • Bioinformatician (NGS/genomics analysis)

  • Computational Biologist (AI/ML applications)

  • Quality Assurance Specialist (manufacturing)

  • Clinical Research Associate (gene therapy trials)

  • Data Scientist (biotech analytics)

INR 50,000 Only for PGP in Bio Tech Genetic Engineering and  AI

Program Architecture & Learning Model

Core Philosophical Approach

The program adopts a cooperative learning model with industry-sponsored projects, where students work with biotechnology professionals on real-world challenges. This approach combines action learning, problem-based learning, and case studies to foster active engagement, deeper understanding, and critical thinking skills. The curriculum balances passive knowledge acquisition with active skill development through a progression from instructor-guided to increasingly independent investigation.​

Program Structure Overview

Total Duration: 60 weeks (15 months)
Format: 100% online with synchronous mentoring and asynchronous content
Delivery Method: Module-based with 2-3 live sessions per week
Expected Weekly Commitment: 20-25 hours (intensive during project phases)
Student Cohort Model: Cohort-based with peer learning and collaboration
Industry Integration: 40+ hours of mentoring, real-world projects in Phases 2, 4, and 5

15-Month Program Competency Development Matrix Across All Phases with Tool Integration and Hours 

Genetic Engineering

  • Phase 1 – Awareness: NCBI, GenBank (20h)

  • Phase 2 – Proficiency: CRISPOR, Benchling, PyMOL (60h)

  • Phase 3 – Advanced: CRISPR optimization, validation (20h)

  • Phase 4 – Expertise: Real‑world implementation (40h)

  • Phase 5 – Mastery: Capstone integration, design projects, peer review (30h)

Bioinformatics & NGS

  • Phase 1 – Awareness: BLAST, sequence databases (25h)

  • Phase 2 – Foundational: Basic alignment, annotation (15h)

  • Phase 3 – Proficiency: FastQC, samtools, GATK, DESeq2 (80h)

  • Phase 4 – Advanced: Pipeline automation, NGS projects (30h)

  • Phase 5 – Expertise: Multi‑omics integration, tool development, data analysis reports (25h)

AI & Machine Learning

  • Phase 1 – Awareness: Python basics, Jupyter, pandas, basic ML concepts (30h)

  • Phase 2 – Foundational: scikit‑learn, TensorFlow basics (20h)

  • Phase 3 – Proficiency: QSAR, deep learning models, product‑level ML (100h)

  • Phase 4 – Advanced: Model deployment, production accuracy tests (40h)

  • Phase 5 – Expertise: Custom architectures, end‑to‑end AI pipelines (35h)

Regulatory & Quality Assurance (QA)

  • Phase 1 – Awareness: Regulatory overview, GMP basics (10h)

  • Phase 2 – Foundational: GMP concepts, documentation (15h)

  • Phase 3 – Foundational: QA principles, auditing (10h)

  • Phase 4 – Proficiency: Regulatory pathways, CTD preparation (50h)

  • Phase 5 – Advanced: Regulatory strategy, submission review, case studies (20h)

Research & Project Management

  • Phase 1 – Foundational: Literature review, planning (25h)

  • Phase 2 – Proficiency: Project execution, reporting (60h)

  • Phase 3 – Advanced: Complex data analysis, interpretation (60h)

  • Phase 4 – Advanced: Industry project management (100h)

  • Phase 5 – Expertise: Capstone project leadership, deliverables, stakeholder reviews (120h)

Professional Communication

  • Phase 1 – Foundational: Scientific writing, presentations (20h)

  • Phase 2 – Proficiency: Project reports, presentations (30h)

  • Phase 3 – Advanced: Documentation, visual communication (25h)

  • Phase 4 – Advanced: Stakeholder communication, pitching (30h)

  • Phase 5 – Expertise: Publication, patent disclosure, high‑stakes presentations (40h)

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PHASE 1: FOUNDATIONS & CORE CONCEPTS (Months 1-3)

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Learning Objectives

Establish strong foundational knowledge in molecular biology, cellular biology, computational basics, and professional development. Students develop skills in scientific communication, understand biotech industry landscape, and prepare computational environments for advanced learning.​

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Month 1: Biotech Landscape, Molecular Biology & Industry Context

Month 2: Molecular Techniques & Cell Biology

Month 3: Genomics & Python Programming

PHASE 2: GENETIC ENGINEERING & MOLECULAR TOOLS (Months 4-6)

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Learning Objectives

Master CRISPR-Cas9 and gene editing technologies through design, validation, and therapeutic application. Develop in silico design expertise, understand bioprocess fundamentals, and complete first comprehensive industry project.​

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Month 4: CRISPR-Cas9 & Gene Editing Mechanisms

Month 5: Advanced Gene Editing & Bioprocessing

Month 6: Industry-Integrated Genetic Engineering Project

PHASE 3: AI/ML INTEGRATION & BIOINFORMATICS (Months 7-9)

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Learning Objectives

Master machine learning algorithms with biotech applications, develop advanced bioinformatics pipeline expertise, and integrate AI tools across drug discovery workflows.​

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Month 7: Machine Learning Foundations & Drug Discovery Applications

Month 8: NGS Analysis & Bioinformatics Pipelines

Month 9: AI in Drug Discovery & Protein Science

PHASE 5: CAPSTONE PROJECT & PROGRAM COMPLETION (Months 13-15)

Learning Objectives

Execute comprehensive capstone project integrating all 15 months of learning. Demonstrate mastery in biotechnology, genetic engineering, and AI/ML application. Prepare for career advancement or research community contribution.

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Month 13: Capstone Project Planning & Development

Months 14: Capstone Execution

Month 15: Final Presentations & Program Completion

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PHASE 4: INDUSTRY PROJECTS & APPLICATIONS (Months 10-12)

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Learning Objectives

Apply all learned competencies to real-world industry challenges through direct collaboration with biotech companies. Develop expertise in quality assurance, regulatory affairs, and professional project execution.​

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Month 10: Industry Integration & Real Project Engagement

Months 11: Real-World Industry Projects

Month 12: Quality Assurance, Regulatory Affairs & Specialization

PROGRAM ASSESSMENT & SKILL DEVELOPMENT FRAMEWORK

Comprehensive Skill Development Matrix

The program systematically develops competencies across six major areas:

Genetic Engineering Proficiency: Students progress from awareness of CRISPR mechanisms in Phase 1 to expert-level therapeutic design and implementation by Phase 5, with specialized tools mastery (CRISPOR, Benchling, PyMOL) integrated throughout.​

Bioinformatics & NGS Analysis: Foundation in sequence analysis (Phase 1) develops into expert-level pipeline development, including NGS preprocessing, variant calling, RNA-Seq analysis, and multi-omics integration by Phase 5.​

AI & Machine Learning: Starting with Python basics (Phase 1), progressing through supervised/unsupervised learning (Phase 3) to expert-level deep learning, model deployment, and AI-driven drug discovery by Phase 5.​

Regulatory & Quality Assurance: GMP basics introduced in Phase 4, developing into proficiency in regulatory pathways, quality systems, and regulatory documentation by Phase 5.​

Research & Project Management: Literature review and planning (Phase 1) develop into expert-level project leadership, capstone project management, and publication preparation by Phase 5.

Professional Communication: Scientific writing (Phase 1) develops into expert-level presentation, stakeholder communication, and publication by Phase 5.

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Assessment Methods

Formative Assessment (Throughout Program):

  • Weekly quizzes and concept checks

  • Assignment feedback and iteration

  • Peer review and discussion forum participation

  • Mentor check-ins and progress feedback

  • Self-assessments and reflection

Summative Assessment (Phase Conclusions):

  • Phase capstone projects (30-40% of phase grade)

  • Technical skill assignments (20-30%)

  • Peer and mentor evaluations (15-25%)

  • Professional presentations (10-15%)

  • Publication/patent outcomes (bonus)

PROGRAM LEARNING OUTCOMES & PROFESSIONAL CREDENTIALS

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Genetic Engineering Mastery:

  • Design, optimize, and validate CRISPR-based therapeutic approaches

  • Evaluate off-target effects and implement mitigation strategies

  • Develop complete therapeutic gene editing strategies from target selection to regulatory pathway

  • Understand manufacturing, scalability, and clinical translation challenges

  • Apply ethical frameworks to genetic modification decisions​

Bioinformatics Expertise:

  • Process and analyze next-generation sequencing data comprehensively

  • Design and implement bioinformatics pipelines for genomic analysis

  • Integrate multi-omics datasets for systems biology insights

  • Conduct variant annotation and clinical interpretation

  • Build reproducible, documented analytical workflows​

AI/ML Proficiency:

  • Develop machine learning models for bioactivity and toxicity prediction

  • Apply deep learning to molecular design and protein structure prediction

  • Implement end-to-end drug discovery pipelines using AI

  • Deploy ML models in production environments

  • Understand limitations and implement explainability methods​

Professional Competence:

  • Navigate FDA and global regulatory pathways for therapeutics

  • Implement Good Manufacturing Practice principles

  • Develop quality management systems and risk assessments

  • Communicate scientific findings to diverse audiences

  • Lead complex biotechnology projects to completion​

PROGRAM ADVANTAGES & DIFFERENTIATION

Unique Program Features

  1. Integrated Approach: Unlike many programs that separate wet-lab skills, bioinformatics, and business, this curriculum integrates all three throughout, reflecting industry reality where professionals must understand the complete biotech value chain.​

  2. Industry-Embedded Learning: 40+ hours of direct mentorship from practicing biotech professionals, real-world projects from actual companies, and direct pathway to employment distinguish this from traditional academic programs.​

  3. Progressive Skill Building: Movement from foundational concepts through theoretical proficiency to expert-level capstone execution follows best practices in inquiry-based education, with demonstrated improvements in retention and competency.​

  4. Project-Based Accountability: Every major concept is applied through hands-on projects, assignments, and industry work, ensuring practical mastery rather than theoretical knowledge.​

  5. Comprehensive Tool Proficiency: Graduates master 30+ industry-standard tools (CRISPOR, Benchling, Python, R, TensorFlow, Docker, etc.) with extensive practice, making them immediately productive in professional settings.

  6. Entrepreneurship Support: Business specialization track and capstone options prepare students for startup creation or intrapreneurship within established companies.​

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