Syllabus
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Course Code: MT-CSE-20-24 Course Name: Elective – IV - (iv) Big Data and Pattern Recognition |
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| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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| 1 | Understanding Big Data: Concepts and Terminology, Big Data Characteristics, Different Types of Data, Identifying Data Characteristics, Business Motivations and Drivers for Big Data Adoption: Business Architecture, Business Process Management, Information and Communication Technology, Big Data Analytics Lifecycle, Enterprise Technologies and Big Data Business Intelligence, Industry examples of big data. |
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| 2 | Big Datasets, Big Data Oversight,Data format, Analyzing Data with Hadoop, Scaling Out, HDFS Concepts, Hadoop I/O, Hadoop Streaming, Design of Hadoop Distributed File System (HDFS), MapReduce Workflows, Unit Tests with MRUnit, Test Data and Local Tests, Anatomy of MapReduce Job Run, Classic Map-Reduce, An Overview of YARN, Zookeeper, HBase, HIVE, Pig, Mahout. Big Data Failure and Legalities. |
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| 3 | Pattern Recognition: Bigotry and Inductive Learning, Bigotry and Inductive Learning, Quantitative and Qualitative Analysis, Pattern Recognition Systems, Fundamental Problems in Pattern Recognition, Feature Extraction and Reduction, Paradigms, Pattern Recognition Approaches, Importance and Applications. Data Domain for Pattern Recognition. Pattern Recognition using Nearest Neighbour Classifier, Classifying using Decision Trees, Obtaining Patterns Rules from Decision Trees. |
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| 4 | An Introduction to NoSQL, Characteristics of NoSQL, Drawbacks, NoSQL Storage Types, Aggregate Data Models, key-value and document data models, relationships, graph databases, schema less databases, materialized views, Data Management for Big Data: Schema Less Models, Key-Value Stores, Document Stores, Tabular Stores, Object Data Stores, Graph databases, The CAP Theorem, NoSQL Misconceptions.. |
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