CSDLO7032: Big Data & Analytics Syllabus for CS 7th Sem 2019 Pattern Mumbai University (Department Level Optional Course-3)

Big Data & Analytics detailed syllabus scheme for Computer Engineering (CS), 2019 regulation has been taken from the MU official website and presented for the Bachelor of Engineering students. For Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below.

For 7th Sem Scheme of Computer Engineering (CS), 2019 Pattern, do visit CS 7th Sem Scheme, 2019 Pattern. For the Department Level Optional Course-3 scheme of 7th Sem 2019 regulation, refer to CS 7th Sem Department Level Optional Course-3 Scheme 2019 Pattern. The detail syllabus for big data & analytics is as follows.

Big Data & Analytics Syllabus for Computer Engineering BE 7th Sem 2019 Pattern Mumbai University

Course Objectives:

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Course Outcomes:

Learner will be able to..

1. Understand the key issues in big data management and its associated applications for business decisions and strategy.
2. Develop problem solving and critical thinking skills in fundamental enabling techniques like Hadoop, Mapreduce and NoSQL in big data analytics.
3. Collect, manage, store, query and analyze various forms of Big Data.
4. Interpret business models and scientific computing paradigms, and apply software tools for big data analytics.
5. Adapt adequate perspectives of big data analytics in various applications like recommender systems, social media applications etc.
6. Solve Complex real world problems in various applications like recommender systems, social media applications, health and medical systems, etc.

Prerequisites:

Some prior knowledge about Java programming, Basics of SQL, Data mining and machine learning methods would be beneficial.

Module 1

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Module 2

Hadoop HDFS and MapReduce

1. Distributed File Systems: Physical Organization of Compute Nodes, Large-Scale File-System Organization.
2. MapReduce: The Map Tasks, Grouping by Key, The Reduce Tasks, Combiners, Details of MapReduce Execution, Coping With Node Failures.
3. Algorithms Using MapReduce: Matrix-Vector Multiplication by MapReduce, Relational-Algebra Operations, Computing Selections by MapReduce, Computing Projections by MapReduce, Union, Intersection, and Difference by MapReduce
4. Hadoop Limitations 10

Module 3

NoSQL

1. Introduction to NoSQL, NoSQL Business Drivers,
2. NoSQL Data Architecture Patterns: Key-value stores, Graph stores, Column family (Bigtable)stores, Document stores, Variations of NoSQL architectural patterns, NoSQL Case Study
3. NoSQL solution for big data, Understanding the types of big data problems; Analyzing big data with a shared-nothing architecture; Choosing distribution models: master-slave versus peer-to-peer; NoSQL systems to handle big data problems. 06

Module 4

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Module 5

Finding Similar Items and Clustering

1. Distance Measures: Definition of a Distance Measure, Euclidean Distances, Jaccard Distance, Cosine Distance, Edit Distance, Hamming Distance.
2. CURE Algorithm, Stream-Computing , A Stream-Clustering Algorithm, Initializing & Merging Buckets, Answering Queries 08

Real-Time Big Data Models

1. PageRank Overview, Efficient computation of PageRank: PageRank Iteration Using MapReduce, Use of Combiners to Consolidate the Result Vector.
2. A Model for Recommendation Systems, Content-Based Recommendations, Collaborative Filtering.
3. Social Networks as Graphs, Clustering of Social-Network Graphs, Direct Discovery of Communities in a social graph.

Text Books:

1. CreAnand Rajaraman and Jeff Ullman Mining of Massive Datasets, Cambridge University Press,
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.

Reference Books:

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
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Suggested Practical List:

1. Hadoop HDFS Practical:
   1. HDFS Basics, Hadoop Ecosystem Tools Overview.
   2. Installing Hadoop.
   3. Copying File to Hadoop.
   4. Copy from Hadoop File system and deleting file.
   5. Moving and displaying files in HDFS.
   6. Programming exercises on Hadoop.
2. Use of Sqoop tool to transfer data between Hadoop and relational database servers.
   1. Sqoop – Installation.
   2. To execute basic commands of Hadoop eco system component Sqoop.
3. To install and configure MongoDB/ Cassandra/ HBase/ Hypertable to execute NoSQL commands.
4. Experiment on Hadoop Map-Reduce / PySpark:
   1. Implementing simple algorithms in Map-Reduce: Matrix multiplication, Aggregates, Joins, Sorting, Searching, etc.
5. Create HIVE Database and Descriptive analytics-basic statistics, visualization using Hive/PIG/R.
6. Write a program to implement word count program using MapReduce.
7. Implementing DGIM algorithm using any Programming Language/ Implement Bloom Filter using any programming language.
8. Implementing any one Clustering algorithm (A-Means/CURE) using Map-Reduce.
9. Streaming data analysis – use flume for data capture, HIVE/PYSpark for analysis of twitter data, chat data, weblog analysis etc.
10. Implement PageRank using Map-Reduce.
11. Implement predictive Analytics techniques (regression / time series, etc) using R/ Scilab/ Tableau/ Rapid miner.
12. Mini Project: One real life large data application to be implemented (Use standard Datasets available on the web).

The Experiments for this course are required to be performed and to be evaluated in Computational Lab-1.

For detail Syllabus of all subjects of Computer Engineering (CS) 7th Sem 2019 regulation, visit CS 7th Sem Subjects of 2019 Pattern.