Course

UNIT 1

Python Programming and Text Processing Foundations

Python string manipulation, regular expressions, text cleaning, tokenisation, file handling, CSV and JSON processing, PDF text extraction, API handling, and Python libraries including re, json, requests, pdfplumber, and pandas.

UNIT 2

Natural Language Processing and MongoDB for Text Understanding

Stopword removal, stemming, lemmatisation, Named Entity Recognition (NER), keyword extraction, sentiment analysis, text vectorisation, document preprocessing, MongoDB collections, CRUD operations, querying and MongoDB integration with Python.

UNIT 3

Text Generation and Summarisation for Multilingual Systems

Pretrained language models, extractive and abstractive summarisation, multilingual translation, response generation, content creation and Python libraries including transformers, deep-translator and torch.

UNIT 4

Semantic Search, Retrieval-Augmented Generation (RAG), and Intelligent Document Systems

Web text extraction, semantic embeddings, cosine similarity, vector databases, document indexing, semantic search pipelines, FAQ systems, and Python libraries including sentence-transformers, langchain, chromadb and faiss.

A. Nature of Course: Theory & Lab

B. Course Objectives:

• The course covers the practical application of Python programming in scientific research, with a focus on developing effective data analysis and literature synthesis skills for exploring Indian textbooks – IKS.
• The subject provides an overview of computational methodologies and their relationship between data analysis, traditional philosophical frameworks, and scholarly communication.
• The course focuses on the application level of programming tools and how they enhance research efficiency, critical thinking, and advanced academic analysis of ancient texts and knowledge systems – IKS.

Programme Outcomes (POs) & Course Outcomes (COs)

• PO1: Disciplinary knowledge: Capable of demonstrating comprehensive knowledge and understanding of one or more disciplines that form a part of the current program.
• PO2: Problem-solving skills: Develop problem-solving skills in familiar and non-familiar contexts and apply one’s learning to real-life situations.
• PO3: Critical and Analytical thinking: Inculcate critical and analytical thinking to analyse and evaluate the reliability and relevance of evidence, scientific arguments, draw valid conclusions, and support them with examples.
• PO4: Scientific reasoning and Research-related skills: Ability to apply scientific reasoning in designing research-related problems, analyse, interpret, and draw conclusions from quantitative/qualitative data. Critically evaluate ideas, evidence, and report the results of an experiment or investigation.
• PO5: Communication Skills and Team work: Develop the individual ability to express thoughts and ideas effectively in writing and orally; and also to communicate with members of diverse teams to work effectively and respectfully.
• PO6: Moral and ethical awareness: Capable of recognizing ethical issues, understanding intellectual property rights, promoting ethical practices in all tasks, and considering environmental and sustainability concerns.
• PO7: Lifelong learning: Ability to acquire knowledge and skills, including self-directed learning, for lifelong learning, personal development, and adapting to evolving workplace demands through continuous skill development and reskilling to meet economic, social, and cultural goals.

Course Outcomes (COs)

• CO 1: Learn the fundamental concepts, capabilities, and syntax of Python programming tools for scientific research.
• CO 2: Acquire knowledge in utilizing Python for literature synthesis and processing digitized texts of Indian Darshanas (summarizing, identifying patterns, methodology extraction).
• CO 3: Evaluate the relationship between computational data analysis, academic integrity, and cognitive independence in philosophical research.
• CO 4: Contemporary research based on the application of Python tools for enhancing academic writing, data analysis, and structural formatting.

C. CO-PO Mapping: [affinity#: 3 – high; 2- moderate; 1- slightly]

Total: 100

• Continuous Assessment Theory: 10% – Assignments and class participation
• Mid-Term Examination: 20% – Lab examination covering theory topics
• Continuous Assessment: Lab: 40% – Project and practical performance
• End Semester Theory Examination: 30% – Lab examination covering the complete syllabus. [50-mark exam: 2 hours].

Name: Dr. Sooraj Rajendran
Designation: Assistant Professor
Email: soorajrajendran@am.amrita.edu

• Sweigart, A. (2019). Automate the boring stuff with Python: Practical programming for total beginners (2nd ed.). No Starch Press.
• Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python. O’Reilly Media.
• Jurafsky, D., & Martin, J. H. (2025). Speech and language processing (3rd ed. draft). Stanford University.
• Banker, K. (2011). MongoDB in action. Manning Publications.
• Chodorow, K. (2019). MongoDB: The definitive guide (3rd ed.). O’Reilly Media.