Big Data Analytics detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the AI&ML students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Machine Learning 5th Sem scheme and its subjects, do visit AI&ML 5th Sem 2021 regulation scheme. For Professional Elective-I scheme and its subjects refer to AI&ML Professional Elective-I syllabus scheme. The detailed syllabus of big data analytics is as follows.
Course Objectives:
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Unit I
UNDERSTANDING BIG DATA
Introduction to big data – convergence of key trends – unstructured data – industry examples of big data – web analytics – big data applications- big data technologies – introduction to Hadoop -open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics.
Unit II
NOSQL DATA MANAGEMENT
Introduction to NoSQL – aggregate data models – key-value and document data models -relationships – graph databases – schemaless databases – materialized views – distribution models – master-slave replication – consistency – Cassandra – Cassandra data model -Cassandra examples – Cassandra clients
Unit III
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Unit IV
BASICS OF HADOOP
Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes -design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow -Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures -Cassandra – Hadoop integration.
Unit V
HADOOP RELATED TOOLS
Hbase – data model and implementations – Hbase clients – Hbase examples – praxis.
Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts.
Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation – HiveQL queries.
Course Outcomes:
After the completion of this course, students will be able to:
- Describe big data and use cases from selected business domains.
- Explain NoSQL big data management.
- Install, configure, and run Hadoop and HDFS.
- Perform map-reduce analytics using Hadoop.
- Use Hadoop-related tools such as HBase, Cassandra, Pig, and Hive for big data analytics.
List of Experiments:
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- Downloading and installing Hadoop; Understanding different Hadoop modes. Startup scripts, Configuration files.
- Hadoop Implementation of file management tasks, such as Adding files and directories, retrieving files and Deleting files
- Implement of Matrix Multiplication with Hadoop Map Reduce
- Run a basic Word Count Map Reduce program to understand Map Reduce Paradigm.
- Installation of Hive along with practice examples.
- Installation of HBase, Installing thrift along with Practice examples
- Practice importing and exporting data from various databases.
Software Requirements:
Cassandra, Hadoop, Java, Pig, Hive and HBase.
Text Books:
- Michael Minelli, Michelle Chambers, and AmbigaDhiraj, “Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses”, Wiley, 2013.
- Eric Sammer, “Hadoop Operations”, O’Reilley, 2012.
- Sadalage, Pramod J. “NoSQL distilled”, 2013
Reference Books:
- E. Capriolo, D. Wampler, and J. Rutherglen, “Programming Hive”, O’Reilley, 2012.
- Lars George, “HBase: The Definitive Guide”, O’Reilley, 2011.
- Eben Hewitt, “Cassandra: The Definitive Guide”, O’Reilley, 2010.
- Alan Gates, “Programming Pig”, O’Reilley, 2011.
For detailed syllabus of all the other subjects of Artificial Intelligence & Machine Learning 5th Sem, visit AI&ML 5th Sem subject syllabuses for 2021 regulation.
For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.