{{{credits}}}
| L | T | P | C |
| 3 | 0 | 0 | 3 |
- To understand the competitive advantages of data analytics.
- To understand data frameworks.
- To learn data analysis methods.
- To learn stream computing.
- To gain knowledge on Hadoop related tools such as HBase, and Hive for data analytics.
{{{unit}}}
| Unit I | Introduction to Big Data | 8 |
Big Data: Definition – Characteristic features – Big data applications – Big data vs traditional data – Risks of big data – Structure of big data; Web data; Evolution of analytic scalability; Modern Data Analytic Tools: R programming.
{{{unit}}}
| Unit II | Hadoop Framework | 9 |
Distributed File Systems: Large-scale file system organization – HDFS concepts – MapReduce execution – Algorithms using MapReduce.
{{{unit}}}
| Unit III | Data Analysis | 11 |
Statistical Methods: Regression modelling – Multivariate analysis; Classification: SVM & Kernel methods; Cluster Analysis: Types of data in cluster analysis – Partitioning methods – Hierarchical methods – Density based methods – Model based clustering methods – Clustering high dimensional data.
{{{unit}}}
| Unit IV | Mining Data Streams | 8 |
Streams: Concepts – Stream data model and architecture – Sampling data in a stream; Mining Data Streams: Filtering streams – Counting distinct elements in a stream – Estimating moments – Counting oneness in a window – Decaying window; Real Time Analytics Platform (RTAP) Applications; Case Studies: Real time sentiment analysis.
{{{unit}}}
| Unit V | Big Data Frameworks | 9 |
Introduction to NoSQL – MongoDB – Aggregate data models – Hbase: Data model and implementations – Hbase clients; Hive: Data types and file formats – HiveQL data definition – HiveQL data manipulation – HiveQL queries.
\hfill Total: 45
After the completion of this course, students will be able to:
- Understand how to leverage the insights from big data analytics (K2).
- Solve applications using statistical and data analytic methods (K3).
- Perform analytics using streaming data (K3).
- Develop applications using Hadoop related tools and R Programming (K4).
- Use database frameworks like MongoDB, Hive and Hbase for data analysis (K3).
After the completion of this course, students will be able to:
- Explain how to leverage the insights from big data analytics (K2).
- Solve applications using statistical and data analytic methods (K3).
- Develop analytics using streaming data (K3).
- Develop applications using Hadoop-related tools and R Programming (K3).
- Develop databases using MongoDB, Hive, and HBase for data analysis (K3).
- Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics”, John Wiley & Sons, 2012.
- Anand Rajaraman and Jeffrey David Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2012.
- Roger D. Peng, “R programming for Data Science”, 5th edition, LeanPub, 2016.
- Michael Berthold and David J. Hand, “Intelligent Data Analysis”, Springer, 2007.
- Tom White, “Hadoop: The Definitive Guide – Storage and Analysis at Internet Scale”, 4th Edition, O’Reilly, 2015.
- E. Capriolo, D. Wampler and J. Rutherglen, “Programming Hive”, O’Reilly, 2012.
- Lars George, “HBase: The Definitive Guide”, O’Reilly, 2011.
- P. J. Sadalage and M. Fowler, “NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence”, Addison - Wesley Professional, 2012.
- Kristina Chodorow, “MongoDB: The Definitive Guide – Powerful and Scalable Data Storage”, 2nd Edition, O’Reilly, 2013.
| PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | ||
| K3 | K6 | K6 | K6 | K6 | ||||||||
| CO1 | K2 | 2 | 1 | 1 | ||||||||
| CO2 | K3 | 3 | 2 | 2 | 2 | 2 | ||||||
| CO3 | K3 | 3 | 2 | 2 | ||||||||
| CO4 | K4 | 3 | 2 | 2 | 2 | 2 | ||||||
| CO5 | K3 | 3 | 2 | 2 | 2 | 2 | ||||||
| Total | 14 | 9 | 9 | 6 | 6 | |||||||
| Course Mapping | 3 | 2 | 2 | 2 | 2 |