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Home / Syllabus / B.E. (Computer Engineering) / Fourth Year CE / Semester 7 / Big Data Analysis

B.E. (Computer Engineering) Big Data Analysis Syllabus - Mumbai University 2026

The University has moved this degree onto NEP 2020 one year at a time. The first and second years are NEP 2020 syllabi; the third and fourth years are still examined on the REV-2019 'C' Scheme, which is what the University sets for them this year.

Big-Data-Analysis.pdf
Semester 7 · Fourth Year CE · 3 credits · 100 marks

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Syllabus for Big Data Analysis

Semester 7 · Fourth Year CE · 3 credits · 100 marks

Module 1: Introduction to Big Data and Hadoop

  • 1.1 Introduction to Big Data - Big Data characteristics and Types of Big Data
  • 1.2 Traditional vs. Big Data business approach
  • 1.3 Case Study of Big Data Solutions
  • 1.4 Concept of Hadoop, Core Hadoop Components; Hadoop Ecosystem

Module 2: Hadoop HDFS and MapReduce

  • Distributed File Systems: Physical Organization of Compute Nodes, Large 2.1 Scale File-System Organization. MapReduce: The Map Tasks, Grouping by Key, The Reduce Tasks, 2.2 Combiners, Details of MapReduce Execution, Coping With Node Failures. Algorithms Using MapReduce: Matrix-Vector Multiplication by MapReduce, Relational-Algebra Operations, Computing Selections by MapReduce, 2.3 Computing Projections by MapReduce, Union ,Intersection, and Difference by MapReduce
  • 2.4 Hadoop Limitations

Module 3: NoSQL

  • 3.1 Introduction to NoSQL, NoSQL Business Drivers NoSQL Data Architecture Patterns: Key-value stores, Graph stores, Column
  • 3.2 family (Bigtable)stores, Document stores, Variations of NoSQL architectural patterns, NoSQL Case Study NoSQL solution for big data, Understanding the types of big data problems; Analyzing big data with a shared-nothing architecture; Choosing distribution 3.3 models: master-slave versus peer-to-peer; NoSQL systems to handle big data problems.

Module 4: Mining Data Streams

  • The Stream Data Model: A Data-Stream-Management System, Examples of 4.1 Stream Sources, Stream Queries, Issues in Stream Processing.
  • 4.2 Sampling Data techniques in a Stream
  • 4.3 Filtering Streams: Bloom Filter with Analysis. Counting Distinct Elements in a Stream,Count
  • 4.4 Distinct Problem, Flajolet-Martin Algorithm, Combining Estimates, Space Requirements Counting Ones in a Window: The Cost of Exact Counts,The
  • 4.5 Datar-Gionis-Indyk-Motwani Algorithm, Query Answering in theDGIM Algorithm, Decaying Windows.

Module 5: Real-Time Big Data Models

  • A Model for Recommendation Systems, Content-Based Recommendations, 5.1 Collaborative Filtering
  • 5.2 Case Study: Product Recommendation Social Networks as Graphs, Clustering of Social-Network Graphs, Direct 5.3 Discovery of Communities in a social graph

Module 6: Data Analytics with R

  • Exploring Basic features of R, Exploring RGUI, Exploring RStudio, Handling Basic Expressions in R, Variables in R, Working with Vectors, Storing and
  • 6.1 Calculating Values in R, Creating and using Objects, Interacting with users, Handling data in R workspace, Executing Scripts, Creating Plots, Accessing help and documentation in R Reading datasets and Exporting data from R, Manipulating and Processing 6.2 Data in R, Using functions instead of script, built-in functions in R
  • 6.3 Data Visualization: Types, Applications

Useful Links

  • 1 Cre Anand Rajaraman and Jeff Ullman ―Mining of Massive Datasets‖, Cambridge UniversityPress
  • 2 Alex Holmes ―Hadoop in Practice‖, Manning Press, Dreamtech Press.
  • 3 Dan Mcary and Ann Kelly ―Making Sense of NoSQL‖ – A guide for managers and the rest of us, Manning Press.
  • 4 DT Editorial Services, “Big Data Black Book”, Dreamtech Press
  • 5 EMC Education Services,”Data Science and Big Data Analytics”,Wiley
  • 1 Bill Franks , ―Taming The Big Data Tidal Wave: Finding Opportunities In HugeData StreamsWithAdvancedAnalytics‖,Wiley
  • 2 Chuck Lam, ―Hadoop inAction‖, Dreamtech Press
  • 3 Jared Dean, ―Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners‖,Wiley India Private Limited, 2014.
  • 4 Jiawei Han and Micheline Kamber, ―Data Mining: Concepts and Techniques‖, Morgan Kaufmann Publishers, 3rd ed, 2010.
  • 5 Lior Rokach and Oded Maimon, ―Data Mining and Knowledge Discovery Handbook‖, Springer, 2nd edition,2010.
  • 6 Ronen Feldman and James Sanger, ―The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data‖, Cambridge University Press, 2006.
  • 7 Vojislav Kecman, ―Learning and Soft Computing‖, MITPress, 2010.
  • 1 https://nptel.ac.in/courses/106104189
  • 2 https://www.coursera.org/specializations/big-data#courses
  • 3 https://www.digimat.in/nptel/courses/video/106106169/L01.html
  • 4 https://www.coursera.org/learn/nosql-databases#syllabus
  • 5 https://www.coursera.org/learn/basic-recommender-systems#syllabus

Reproduced from the University of Mumbai syllabus for B.E. (Computer Engineering) under REV-2019 'C' Scheme, in force from the academic year 2022-23. 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 7.9 (R-A) B.E. (Computer Engineering) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.24 (N) B.E. (Computer Engineering) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.15 B.E. (Computer Engineering) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF 6.41 (R) B.E. (Computer Engineering) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
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