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Home / Syllabus / B.Sc. (Data Science) / TY BSc Data Science / Semester 6 / Natural Language Processing

B.Sc. (Data Science) Natural Language Processing Syllabus - Mumbai University

This is the TY BSc Data Science syllabus under NEP 2020, in force from the academic year 2026-27. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.

Natural Language Processing Syllabus.pdf
Major · Semester 6 · TY BSc Data Science · 2 credits · 50 marks

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Syllabus for Natural Language Processing

Major · Semester 6 · TY BSc Data Science · 2 credits · 50 marks

Module I: Foundations of Natural Language Processing

  • Introduction to Natural Language Processing
  • Overview of NLP
  • History and evolution of NLP
  • Relationship between Text Analytics and NLP
  • Applications of NLP NLP Processing Pipeline
  • Steps in an NLP system
  • Data collection and preprocessing
  • Feature extraction
  • Model development, evaluation, and deployment Text Preprocessing Techniques
  • Tokenization
  • Part-of-Speech (POS) tagging
  • Stop-word removal
  • Text normalization
  • Spelling correction
  • Stemming and Lemmatization
  • Sentence boundary detection
  • Named Entity Recognition
  • Word Sense Disambiguation Feature Extraction from Text
  • Types of textual data and text cleaning
  • N-grams and tokenization methods
  • Bag of Words model
  • Zipf’s Law
  • TF–IDF representation
  • Feature engineering and text similarity
  • Text visualization techniques (word clouds, dependency trees)

Module II: Machine Learning and Applications in NLP

  • Machine Learning for NLP
  • Supervised and unsupervised learning
  • Hierarchical clustering and K-means clustering
  • Classification algorithms o Logistic Regression o Naïve Bayes o K-Nearest Neighbors Advanced Machine Learning Models
  • Regression with textual data
  • Tree-based models o Decision Trees o Random Forest o Gradient Boosting o XGBoost
  • Sampling techniques
  • Dimensionality reduction (PCA)
  • Model evaluation and performance metrics
  • NLP pipelines and model persistence Text Data Collection and Processing
  • Web scraping
  • Extracting data from HTML
  • Working with JSON and XML data
  • Using APIs for real-time data collection
  • Extracting text from local files Applications of NLP
  • Topic modeling o Latent Semantic Analysis (LSA) o Latent Dirichlet Allocation (LDA)
  • Text summarization o Extractive and abstractive summarization o TextRank algorithm
  • Text generation using Markov chains
  • Vector representations of text o One-hot encoding o Word embeddings and Word2Vec o Document vectors
  • Sentiment analysis o Sentiment analysis concepts and applications o Tools and libraries for sentiment analysis o Training sentiment classification models 10 Text Books 1. Natural Language Processing Fundamentals, Sohom Ghosh and Dwight Gunning Published by Packt Publishing Ltd. March 2019 2. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit Sebastopol, CA: O’Reilly Media.2009 3. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models (3rd ed.). Online manuscript. Stanford University 11 Reference Books 1. Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press. 1999 2. Introduction to Natural Language Processing.Cambridge, MA: MIT Press. 2019 12 Internal Continuous Assessment: Semester End Examination: 60% 40% 13 Continuous Evaluation through: 30 marks Semester End Examination Lecture attendance, Mid Term teste, assignment, class test, presentations on new real life applications and its working 14 Format of Question Paper: (Semester End Examination: 30 Marks. Duration: 1 Hr ) Q1: Attempt any TWO (out of four) from Module 1 (15 Marks) Q2: Attempt any TWO (out of four) from Module 2 (15 Marks)

Text Books

  • 1 Natural Language Processing Fundamentals, Sohom Ghosh and Dwight Gunning Published by Packt Publishing Ltd. March 2019
  • 2 Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit Sebastopol, CA: O’Reilly Media.2009
  • 3 Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models (3rd ed.). Online manuscript. Stanford University
  • 1 Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press. 1999
  • 2 Introduction to Natural Language Processing.Cambridge, MA: MIT Press. 2019

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020, in force from the academic year 2026-27. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 2024 25 DS SEM I & II NEP NEP 2020 syllabus, in force from 2024-25 Read full PDF Read
PDF 2023 24 BSc Data Science Sem V & VI Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read
PDF 2021 22 BSc Data Science Sem III & IV Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read
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