Big Data Analytics detailed syllabus for Geoinformatics Engineering (Geoinformatics) for 2019 regulation curriculum has been taken from the Anna Universities official website and presented for the Geoinformatics 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 Geoinformatics Engineering 8th Sem scheme and its subjects, do visit Geoinformatics 8th Sem 2019 regulation scheme. For Professional Elective-VII scheme and its subjects refer to Geoinformatics Professional Elective-VII syllabus scheme. The detailed syllabus of big data analytics is as follows.
Course Objective:
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.
Unit I
Introduction To Big Data
Evolution of Big data – Best Practices for Big data Analytics – Big data characteristics – Validating – The Promotion of the Value of Big Data – Big Data Use Cases- Characteristics of Big Data Applications -Perception and Quantification of Value -Understanding Big Data Storage – A General Overview of High-Performance Architecture – HDFS – MapReduce and YARN – Map Reduce Programming Model
Unit II
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
Unit III
Association and Recommendation System
Advanced Analytical Theory and Methods: Association Rules – Overview – Apriori Algorithm – Evaluation of Candidate Rules – Applications of Association Rules – Finding Association& finding similarity -Recommendation System: Collaborative Recommendation- Content Based Recommendation -Knowledge Based Recommendation- Hybrid Recommendation Approaches.
Unit IV
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
Unit V
Nosql Data Management for Big Data and Visualization
NoSQL Databases : Schema-less Models||: Increasing Flexibility for Data Manipulation-Key Value Stores- Document Stores – Tabular Stores – Object Data Stores – Graph Databases Hive – Sharding –Hbase – Analyzing big data with twitter – Big data for E-Commerce Big data for blogs – Review of Basic Data Analytic Methods using R.
Course Outcome:
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
Text Books:
- Anand Rajaraman and Jeffrey David Ullman, “Mir,iina of Massive Datasets”, Cambridge . University Press, 2012.
- David Loshin, “Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph”, Morgan Kaufmann/El sevier Publishers, 2013.
References:
- EMC Education Services, “Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data”, Wiley publishers, 2015.
- Bart Baesens, “Analytics in a Big Data World: The Essential Guide to Data Science and its Applications”, Wiley Publishers, 2015.
- Dietmar Jannach and Markus Zanker, “Recommender Systems: An Introduction”, Cambridge University Press, 2010.
- Kim H. Pries and Robert Dunnigan, “Big Data Analytics: A Practical Guide for Managers ” CRC Press, 2015.
- Jimmy Lin and Chris Dyer, “Data-Intensive Text Processing with MapReduce”, Synthesis Lectures on Human Language Technologies, Vol. 3, No. 1, Pages 1-177, Morgan Claypool publishers, 2010.
For detailed syllabus of all the other subjects of Geoinformatics Engineering 8th Sem, visit Geoinformatics 8th Sem subject syllabuses for 2019 regulation.
For all Geoinformatics Engineering results, visit Anna University Geoinformatics all semester results direct link.