B.Sc. (Data Science) BigData and AIML Practical Syllabus - Mumbai University 2026

This is the SY BSc Data Science syllabus under NEP 2020, in force from the academic year 2025-26. 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 BigData and AIML Practical

Major · Semester 4 · SY BSc Data Science · 2 credits · 50 marks

Module I: Big Data Practical

Install and configure Hadoop in pseudo-distributed mode on your local machine or cloud platform. Demonstrate file upload to HDFS, retrieval from HDFS, and basic file operations using Hadoop commands.
Write a MapReduce program to count word frequencies from a large dataset stored in HDFS. Execute and analyze the job logs to identify the mapper and reducer outputs.
Implement a MapReduce program to process a financial transactions dataset and compute the total transaction amount for each customer using key-value pairs.
Set up Apache Spark on a local machine or cloud environment. Load a sample dataset into an RDD and demonstrate basic transformations and actions such as map, filter, and reduce.
Use Spark DataFrame API to read a large JSON dataset, filter data based on specific conditions, and perform aggregation to find average, min, and max values.
Perform real-time stream processing using Spark Streaming by ingesting a live data stream (e.g., Twitter feed or socket stream) and compute word frequency counts in real-time.
Install and configure Apache Flink. Implement a Flink streaming application to read streaming data from a text socket and compute the running count of words in real-time.
Set up a NoSQL database such as MongoDB or Cassandra. Create a collection/table, insert large amounts of sample data, and demonstrate queries for data retrieval, aggregation, and filtering.
Compare the data models of different NoSQL databases (MongoDB, Cassandra, HBase) by designing a schema for a sample e-commerce application and evaluating read and write latencies.
Implement a Hadoop job scheduler configuration and demonstrate how to manage multiple MapReduce jobs using YARN resource manager in a multi-tenant cluster.
Configure a multi-node Hadoop cluster using Docker or cloud instances. Submit a large MapReduce job and monitor the cluster resource utilization using the ResourceManager UI.
Demonstrate performance tuning techniques in Hadoop such as block size optimization, combiner usage, speculative execution management, and mapper/reducer tuning on a large dataset.
Implement a custom resource management policy for local resource consumption shaping using MapReduce, where jobs are throttled based on CPU or memory usage constraints.
Set up HBase on Hadoop and demonstrate data insertion, update, and retrieval for a time-series dataset representing sensor data.
Simulate a multi-core environment using a virtualization tool or cloud VM and write a program to replay and analyze data-intensive tasks using packing algorithms to optimize core usage

Module II: AIML Practical AI Practical

Write a program to implement depth first search algorithm.
Write a program to implement breadth first search algorithm.
State the water jug problem. Write a program to solve the water jug problem.
Write a program to simulate the N-Queen problem.
Solve travelling salesman problems using artificial intelligence techniques.
Write a program to solve the Tower of Hanoi problem.
Solve the block of World problem.
Design the simulation of tic – tac – toe game using min-max algorithm.
Write a program to implement alpha beta search.
Write a program for a Hill climbing problem.
ML Practical
Implementing a K-Nearest Neighbor (KNN) algorithm.
Building a decision tree model using the ID3 algorithm.
Developing a Support Vector Machine (SVM) model.
Building a Naïve Bayes classifier.
Implementing linear regression.
Perform prediction using logistic regression.
Evaluating a classification model using metrics such as accuracy, precision, recall, and F1 score.
Perform segmentation by applying hierarchical clustering.
Implementing the K-means clustering algorithm.
Utilizing Principal Component Analysis (PCA) for dimensionality reduction to improve the efficiency and interpretability of a model. (Consider the relevant datasets for implementation of AI/Machine learning practical)

Textbooks

1 Python Machine learning for Beginners, AI Publishing LLC, First 2020.
2 Hands of Machine learning with Scikit-Learn, Keras, and Tensorflow, Aurilien Geron, O’Reilly Publication, Third Edition, 2022.
1 Artificial Intelligence: A Modern Approach, Third Edition, Stuart J. Russell and Peter Norvig, 2010
2 Introduction to Artificial Intelligence, Marc Toussaint February 4, 2019
3 Artificial Intelligence, Elaine Rich, Kevin Knight, & Shivashankar B Nair, McGraw Hill, 3 rd ed.,2009
4 Introduction to Artificial Intelligence & Expert Systems, Dan W Patterson, PHI.,2010
5 Python Machine Learning, Sebastian Raschka and Vahid Mirjalili, Packt Publication 2021
6 Machine Learning, Saikat Dutt, Subramanian Chandramouli, Amit Kumar Das, First Edition, Pearson Education 2018.

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020, in force from the academic year 2025-26. 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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