📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Generative AI & LLMs Practicum for Science, from foundations to a certified capstone project.
Outline
Understand how Generative AI and Large Language Models support scientific research, learning, analysis, and documentation. • Learn key concepts such as prompts, tokens, context windows, model outputs, hallucination, reasoning, retrieval, and AI-assisted workflows. • Explore practical uses of LLMs in biotechnology, healthcare, chemistry, materials science, environmental science, and data-driven research.
Outline
Design clear and structured prompts for scientific explanations, summaries, comparisons, and research planning. • Use role-based prompting, structured instructions, examples, constraints, and output formats for better AI responses. • Build reusable prompt templates for literature review, hypothesis generation, protocol drafting, and scientific communication.
Outline
Use Generative AI to summarize research articles, extract key themes, compare studies, and identify research gaps. • Organize scientific knowledge into outlines, tables, concept maps, and structured review notes. • Learn how to validate AI-generated literature summaries using source checking and human review.
Outline
Draft scientific abstracts, introductions, reports, explanations, lab notes, and technical summaries using AI assistance. • Improve clarity, structure, grammar, readability, and scientific tone without changing the meaning of the content. • Use LLMs for research proposal outlines, presentation scripts, manuscript planning, and documentation workflows.
Outline
Use AI to interpret tables, experimental observations, analytical summaries, and research datasets. • Convert scientific data into readable explanations, insights, limitations, and decision-support notes. • Understand the role of human validation when using AI for scientific data interpretation and reporting.
Outline
Understand how Retrieval-Augmented Generation helps connect LLMs with trusted scientific documents and knowledge bases. • Explore how RAG supports literature search, document Q&A, research summarization, and scientific decision support. • Learn basic workflows for grounding AI responses in reliable scientific sources and reducing hallucination risks.
Outline
Use Generative AI to plan experiments, prepare checklists, design workflows, and identify possible limitations. • Generate structured research questions, hypotheses, variables, controls, and expected observations. • Apply LLMs to simplify complex scientific concepts and support interdisciplinary problem solving.
Outline
Understand hallucination, bias, privacy, plagiarism, citation misuse, data sensitivity, and responsible AI practices in science. • Learn how to fact-check AI outputs, verify scientific claims, and maintain academic integrity. • Apply human review, source validation, and ethical documentation when using AI in scientific workflows.
Outline
Work on a complete science-focused AI workflow involving literature review, prompt design, data interpretation, and report creation. • Create AI-assisted outputs such as summaries, research maps, experiment plans, documentation, and presentation material. • Build a portfolio-ready practicum project demonstrating responsible and practical use of Generative AI and LLMs in science.
Earn government-registered certification in Generative AI & LLMs Practicum for Science
e-Certificate and e-Marksheet issued on successful completion.
