Page 74 - B.Tech IT Curriculum and Syllabus R2017 - REC

Department of IT, REC

UNIT IV MINING DATA STREAMS 9
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 - Realtime Analytics Platform(RTAP)
applications - case studies - real time sentiment analysis, stock marketpredictions.

UNIT V DATA ANALYSIS AND VISUALIZATION 12
Regression modelling, Multivariate analysis, Decision Trees, Support vector and kernel methods,
Neural networks: learning and generalization, competitive learning, principal component analysis and
neural networks; Clustering Techniques – Hierarchical – K- Means – Clustering high dimensional
data – Frequent pattern based clustering methods – Clustering in Non-Euclidean space – Clustering
for streams and Parallelism- Visualization - Time series analysis.

TOTAL: 45 PERIODS

OUTCOMES:
At the end of the course, student will be able to:
1. understand the usage scenarios of Big Data Analysis and Hadoop framework
2. Apply Mapreduce over HDFS
3. Make design decisions for choice of NoSQL platforms to build applications
4. Apply Stream Data Model
5. Use various data analysis techniques

TEXT BOOKS:
1. Seema Acharya, Subhasini Chellappan, "Big Data Analytics" Wiley India; First Edition ,
2015.
2. Anand Rajaraman and Jeffrey David Ulman, ―Mining of Massive Datasets‖, Cambridge
University Press, First Edition , 2012.
3. Jiawei Han, Micheline Kamber ―Data Mining Concepts and Techniques‖, Second Edition,
Elsevier, Reprinted 2008.
4. Michael Berthold, David J. Hand, "Intelligent Data Analysis‖, Second Edition, Springer, 2007.

REFERENCES:
1. Jay Liebowitz, ―Big Data and Business Analytics‖ Auerbach Publications, CRC press First
Edition, 2013.
2. Tom White ― Hadoop: The Definitive Guide‖ Third Edition, O‘Reilly Media, 2012.
3. Bill Franks, ―Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams
with Advanced Analytics‖, John Wiley & sons, First Edition, 2012.

IT17711 DATA ANALYTICS LABORATORY L T P C
0 0 4 2

OBJECTIVES:
The student should be made to:
● Implement Map Reduce concept to process big data.
● Apply linear models to analyze big data.
● Analyze big data using machine learning techniques.
● Realize storage of big data using Hbase, MongoDB.
● Develop big data applications for streaming data using Apache Spark.

Curriculum and Syllabus | B.Tech. Information Technology | R2017 Page 74