B.Sc. (Data Science) Major Practical NLP and Sports Analytics Practical 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.

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Syllabus for Major Practical - NLP and Sports Analytics Practical

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

Module I: Natural Language Processing Practical

Practical 1 - Python Basics for NLP: Introduction to Python for text processing, String operations (split, join, replace), File handling (read/write text files), Simple text cleaning using Python Practical 2 - NLP Setup & Tokenization: Install NLP libraries (NLTK/TextBlob/Scikit- learn), perform basic text analytics, tokenize sentences and paragraphs, and inspect tokens using Python scripts. Practical 3 - Regular Expressions for Text Processing: Pattern matching using re, Extract emails, phone numbers, URLs, Cleaning text using regex Practical 4 - NLP Preprocessing: Perform POS tagging, remove stop words, detect sentence boundaries, and extract named entities from sample text datasets. Practical 5 - Text Normalization: Implement text normalization, perform spelling correction, apply stemming (Porter/Regexp), and perform lemmatization while comparing results. Practical 6 - Feature Extraction: Generate n-grams (bi-grams and tri-grams), tokenize text using different libraries, remove noise from text, and extract basic statistical text features. Practical 7 - Text Representation: Implement Bag-of-Words models, generate TF-IDF vectors, and compute similarity between text documents using vector representations. Practical 8 - Text Visualization: Generate word clouds, analyze word frequency distribution using Zipf’s Law, and visualize dependency parse trees and named entities. Practical 9 - Machine Learning for NLP: Build text classification models using Logistic Regression, Naive Bayes, and K-Nearest Neighbors; split datasets into training and testing sets; and compare model performance. Practical 10 - Advanced ML Models: Train Decision Tree and Random Forest classifiers, implement Gradient Boosting/XGBoost for text classification, and compare model accuracy and performance metrics. Practical 11 - Dimensionality Reduction: PCA for text features, Feature reduction visualization, Compare performance before & after PCA Practical 12 - NLP Pipeline Development: Build an end-to-end NLP pipeline including preprocessing, feature extraction, dimensionality reduction (PCA), model training, and model evaluation. Practical 13 - Web Data Collection: Scrape textual data from HTML pages, extract content using Python requests and BeautifulSoup, retrieve data using APIs, and parse JSON datasets. Practical 14 - Topic Modeling: Perform topic modeling using Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA), analyze document clusters, and visualize topic distributions across documents. Practical 15 - Sentiment Analysis & Text Summarization: Perform sentiment analysis using TextBlob, train a sentiment classification model, and implement automatic text summarization using Gensim or frequency-based techniques.

Module II: Sports Data Analytics Practical’s

Data Collection, Cleaning and Preprocessing in Sports Analytics Practical 1: Sports Dataset Import and Data Cleaning( Import match or player dataset, Identify missing values, Remove duplicate records, Standardize player names and date formats) Practical 2: Data Transformation and Feature Engineering( Normalize performance metrics, Apply Min-Max scaling and standardization, Encode categorical variables (team, position). Create derived features (performance index, workload ratio)) Practical 3: Exploratory Data Analysis in Sports Performance( Generate summary statistics, Correlation analysis, Boxplot and histogram, visualization, Detect and treat outliers) Performance Metrics and Statistical Evaluation Practical 4: Calculation of Key Performance Indicators (KPIs) (Compute scoring averages, Calculate efficiency ratings, Create composite, performance score, Compare multiple players, Practical 5: Statistical Comparison of Team Performance (Perform hypothesis testing (t- test), Analyze variance in team performance, Study match-by-match performance trends) Practical 6: Sports Performance Visualization Dashboard (Create player comparison charts, Plot seasonal performance trends, Visualize team efficiency metrics, Design simple analytical dashboard) Predictive Modelling and Game Analytics Practical 7: Match Outcome Prediction using Classification(Train Logistic Regression model, Predict win/loss outcome, evaluate model using accuracy and confusion matrix) Practical 8: Player Performance Prediction using Regression(Apply Linear Regression model, Predict future player performance, Compute RMSE and R² score Practical 9: Player Profiling using Clustering( Apply K-Means clustering, Group players based on performance statistics, Visualize clusters and interpret results Injury Risk and Workload Analytics Practical 10: Athlete Workload Monitoring Analysis( Analyze training load dataset, Calculate Acute: Chronic Workload Ratio, Identify high-risk workload levels Practical 11: Injury Risk Prediction Model( Train Random Forest classifier, Predict injury probability, Evaluate precision, recall and F1-score) Practical 12: Anomaly Detection in Athlete Health Data( Use Isolation Forest or One- Class SVM, Detect abnormal performance or fatigue levels, Visualize anomaly patterns AI Applications and Fan Engagement Analytics Practical 13: Opponent Performance Pattern Analysis( Analyze match event dataset, Identify scoring patterns, Predict likely scoring zones using ML model) Practical 14: Social Media Fan Engagement Analytics( Analyze engagement dataset, Compute engagement rate, Perform basic sentiment analysis) Practical 15: Comparative Evaluation of Machine Learning Models( Compare Logistic Regression, Random Forest and SVM, Evaluate using accuracy, precision and F1-score, Select optimal model for sports prediction) 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. Sports Data Analytics: Techniques, Applications, and Innovations, edited by Mehul S. Raval, Tolga Kaya, N. Sertac Artan, and Christopher Taber, published by Springer Nature (Studies in Big Data Series), 2026, First Edition. 4. Machine Learning and Data Mining for Sports Analytics: 9th International Workshop, MLSA 2022 Proceedings, edited volume (workshop proceedings), published by Springer, 2022, First Edition. 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 3. Handbook of Statistical Methods and Analyses in Sports, by Jim Albert, Mark E. Glickman, Tim B. Swartz, and Ruud H. Koning, published by Chapman & Hall/CRC Press, 2017, First Edition. 12 Internal Continuous Assessment: 40% Semester End Examination: 60% 13 Continuous Evaluation through: 30 marks Semester End Examination Students are expected to attend each practical and submit the written practical of the previous session. Performing Practical and writeup submission will be continuous internal evaluation. 2.5 marks can be awarded for each practical performance and writeup submission totaling to 50 marks and can be converted to 20 marks. 14 Format of Question Paper: (Semester End Examination : 30 Marks. Duration: 2 Hours)

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 Sports Data Analytics: Techniques, Applications, and Innovations, edited by Mehul S. Raval, Tolga Kaya, N. Sertac Artan, and Christopher Taber, published by Springer Nature (Studies in Big Data Series), 2026, First Edition.
4 Machine Learning and Data Mining for Sports Analytics: 9th International Workshop, MLSA 2022 Proceedings, edited volume (workshop proceedings), published by Springer, 2022, First Edition.
1 Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press. 1999
2 Introduction to Natural Language Processing.Cambridge, MA: MIT Press. 2019
3 Handbook of Statistical Methods and Analyses in Sports, by Jim Albert, Mark E. Glickman, Tim B. Swartz, and Ruud H. Koning, published by Chapman & Hall/CRC Press, 2017, First Edition.

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.

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