Syllabus
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Course Code: MS-20-43 Course Name: Elective-III (ii) - 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 | Data Governance for Big Data Analytics: Evolution of Data Governance, Big Data and Data Governance, Big Datasets, Big Data Oversight, Big Data Tools and Techniques: HDFS, Map Reduce, YARN, Zookeeper, HBase, HIVE, Pig, Mahout, Developing Big Data Applications, Stepwise Approach to Big Data Analysis, Big Data Failure: Failure is common, Failed Standards, Legalities. |
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| 3 | Pattern Recognition: Preview of Inductive Learning, Bigotry and Inductive Learning, Pattern Recognition Systems, Fundamental Problems in Pattern Recognition, Feature Extraction and Reduction, Paradigms, Pattern Recognition Approaches, Importance and Applications. Classifying using Decision Trees, Obtaining Patterns Rules from Decision Trees, Syntactic Pattern Recognition. |
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| 4 | An Overview of NoSQL, Characteristics of NoSQL, NoSQL Storage Types, Advantages and Drawbacks, Comparison of NoSQL Products, The CAP Theorem, Partitioning, Storage Layout, Introduction to Key-Value Store, Document Databases and Column-Oriented Databases, NoSQL Misconceptions, NoSQL over RDBMS. |
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