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
This 3‑day intensive program equips researchers, PhD scholars, faculty, and professionals with the knowledge and skills to responsibly harness Generative AI in academic and scientific research. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This 3‑day intensive program equips researchers, PhD scholars, faculty, and professionals with the knowledge and skills to responsibly harness Generative AI in academic and scientific research.
📋 Course Objectives
1. Put biotechnology techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A portfolio-grade biotechnology 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
Day 1 – Introduction to Generative AI in Academic Research
Explore the landscape of Generative AI and its research applications • Identify ethical risks such as bias and hallucinations • Automate literature reviews and research ideation with AI • Ensure academic integrity while using AI tools
Day 2 – AI‑Assisted Academic Writing & Data Analysis
Structure and draft manuscripts using AI prompts • Enhance grammar, coherence, and readability with AI checks • Perform basic data analysis and pattern detection in Python • Visualize research findings through AI‑driven graphics
Day 3 – AI in Peer Review, Integrity & Productivity
Automate peer‑review checks and manuscript quality assessment • Detect predatory publishing using AI classifiers • Boost research productivity by automating repetitive tasks • Build reproducible, transparent workflows with Jupyter Notebooks
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | OpenAI API |
| Covered Tool / Platform | Generative AI Tools |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | Scite.ai |
| Covered Tool / Platform | Jupyter Notebooks |
Frequently Asked Questions
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