B.Sc. (Computer Science) Data Science Syllabus - Mumbai University
This is the TY BSc Computer Science syllabus under NEP 2020, in force from the academic year 2026-27. The University still sets the earlier Choice Based papers alongside it — her Summer 2026 third-year timetables name that scheme — so check which scheme your exam form names before you revise.
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Syllabus for Data Science
The University sets the practical for this subject separately, in Computer Science Practical 6, as Module 1. It carries its own credits, so it is examined as a paper of its own.
Module I
- Introduction to Data Science: Definition and scope of Data Science, Applications and domains of Data Science, Comparison with other fields like Business Intelligence (BI), Artificial Intelligence (AI), Machine Learning (ML), and Data Warehousing/Data Mining (DW-DM), Different types of data: structured, unstructured, semi-structured; Data sources: databases, files, APIs, web scraping, sensors, social media
- Data Preprocessing: Data cleaning: handling missing values, outliers, duplicates; Data transformation: scaling, normalization, encoding categorical variables; Feature selection: selecting relevant features/columns; Data merging: combining multiple datasets; Data wrangling techniques: reshaping, pivoting, aggregating
- Exploratory Data Analysis (EDA): Feature engineering: creating new features, handling time-series data; Dummification: converting categorical variables into binary indicators; Feature scaling: standardization, normalization; Data visualization techniques: histograms, scatter plots, box plots, etc.; Descriptive statistics: mean, median, mode, standard deviation, etc.; Hypothesis testing: t-tests, chi square tests, ANOVA, etc.
- Machine Learning Algorithms: Supervised learning: classification and regression, Unsupervised learning: clustering and dimensionality reduction, Bias-variance tradeoff, underfitting, and overfitting, Simple linear regression, Multiple linear regression, Stepwise regression, Logistic regression for classification, Decision Trees and Random Forests, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Ensemble Learning: Boosting and Bagging, K-Nearest Neighbors (K-NN), Gradient Descent for optimization.
Module II
- Model Evaluation, Selection and Metrics: Techniques for evaluating, model performance: accuracy, precision, recall, F1-score, Confusion, matrix and ROC – AUC analysis, evaluating models for imbalanced, datasets, Cross-validation: k-fold cross-validation, stratified cross- validation, Hyperparameter tuning and model selection
- Data Visualization and Communication: Principles of effective data visualization, Types of visualizations: bar charts, line charts, scatter plots, etc. Data storytelling: communicating insights through visualizations
- Tools and Libraries: Introduction to popular libraries and technologies, used in Data Science like Pandas, NumPy, Sci-kit Learn, etc. Visualization tools: matplotlib, seaborn, Tableau, etc.
- Data Management: Introduction to data management activities, Data pipelines: data extraction, transformation, and loading (ETL), Data governance and data quality assurance, Data privacy and security considerations
- Data Ethics and Privacy: Data lifecycle ethics (collection, use, reuse), Privacy risks and consent, Basic overview of GDPR and DPDP Act 2023, Responsible data handling practices
Text Books
- 1 Data Science from Scratch First Principles with Python- Joel Grus O’reilly, 2nd Edition
- 2 Advancing into Analytics From Excel to Python and R, George Mount, Oreilly, First Edition
- 3 Introduction to Machine Learning with Python, Andreas C. Muller, Sarah Guido, Oreilly, First Edition
- 1 Doing Data Science, Rachel Schutt and Cathy O’Neil, O’Reilly,2013
- 2 Mastering Machine Learning with R, Cory Lesmeister, PACKT Publication,2015
- 3 Hands-On Programming with R, Garrett Grolemund,1st Edition, 2014
- 4 An Introduction to Statistical Learning, James, G., Witten, D., Hastie, T., Tibshirani, R.,Springer,2015
Reproduced from the University of Mumbai syllabus for B.Sc. (Computer 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.