JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Prerequisite

Nil

Course Objectives

The student should be made to :

• Be exposed to conceptual frame work of big data.
• Understand different techniques of Data Analysis.
• Be familiar with concepts of data streams.
• Be exposed to item sets, Clustering, frame works and Visualization.

Course Outcomes

Upon completion of this course the students will be able to

• Understand Big data fundamentals.
• Learn various Data Analysis Techniques
• Implement various Data streams.
• Understand item sets, Clustering, frame works & Visualizations.

UNIT - I

Introduction to Big Data: Introduction to Big Data Platform – Challenges of Conventional systems – Web data – Evolution of Analytic scalability , analytic process and tools , Analysis vs Reporting – Modern data analytic tools, stastical concepts : Sampling distributions, resampling , statistical inference, prediction error.

UNIT - II

Data Analysis: Regression modeling , Multivariate analysis, Bayesian modeling , inference and Bayesian networks , Support vector and Kernel methods, Analysis of time series : Linear systems analysis , nonlinear dynamics – Rule induction – Neural networks : Learning and and Generalisation, competitive learning, Principal component analysis and neural networks ; Fuzzy Logic : extracting fuzzy models from data , fuzzy decision trees, Stochastic search methods.

UNIT - III

Mining Data Streams: Introduction to Streams Concepts – Stream data model and architecture – Stream Computing , Sampling data in a stream – 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, stock market predictions.

R16 B.TECH AERONAUTICAL ENGG.

UNIT - IV

Frequent Itemsets and clustering: Mining Frequent itemsets – Market based Modeling – Apriori Algorithm – Handling large data sets in Main Memory – Limited Pass Algorithm – Counting frequent itemsets in a Stream – Clustering Techniques – Hierarchical – K-Means – Clustering high dimensional data – CLIQUE and ProCLUS – Frequent pattern based clustering methods – Clustering in non-euclidean space – Clustering for streams and Parallelism.

UNIT - V

Frame Works and Visualization: MapReduce – Hadoop, Hive , MapR – Sharding – NoSQL Databases – S3 – Hadoop Distributed file systems – Visualizations – Visual data analysis techniques, interaction techniques : systems and Applications .

TEXT BOOKS:

• 1. Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer, 2007.
• 2. Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press, 2012.

REFERENCE BOOKS:

• 1. Bill Franks, Taming the Big Data Tidal wave: Finding Opportunities in Huge Data Streams with advanced analytics, John Wiley & sons, 2012.
• 2. Glenn J. Myatt, Making Sense of Data, John Wiley & sons, 2007 Pete Warden, Big Data Glossary, O’Reilly, 2011.
• 3. Jiawei Han, Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition, Elsevier, Reprinted 2008. R16 B.TECH AERONAUTICAL ENGG. B.TECH ELECTRICAL AND ELECTRONICS ENGINEERING