ANNA UNIVERSITY CSE/IT SYLLABUS
IT6006 DATA ANALYTICS SYLLABUS
7TH SEM CSE/IT SYLLABUS
REGULATION 2013
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| IT6006 DATA ANALYTICS SYLLABUS |
OBJECTIVES:
The Student should be made to:
-> Be exposed to big data
-> Learn the different ways of Data Analysis
-> Be familiar with data streams
-> Learn the mining and clustering
-> Be familiar with the visualization
The Student should be made to:
-> Be exposed to big data
-> Learn the different ways of Data Analysis
-> Be familiar with data streams
-> Learn the mining and clustering
-> Be familiar with the visualization
UNIT I INTRODUCTION TO BIG DATA
Introduction to Big Data Platform – Challenges of conventional systems - Web data – Evolution of Analytic scalability, analytic processes 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 generalization, 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 - Realtime Analytics Platform(RTAP) applications - case studies - real time sentiment analysis, stock market predictions.
UNIT IV FREQUENT ITEMSETS AND CLUSTERING
Mining Frequent itemsets - Market based model – 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 FRAMEWORKS AND VISUALIZATION
MapReduce – Hadoop, Hive, MapR – Sharding – NoSQL Databases - S3 - Hadoop Distributed file systems – Visualizations - Visual data analysis techniques, interaction techniques; Systems and applications:
TOTAL: 45 PERIODS
OUTCOMES:
The student should be made to:
-> Apply the statistical analysis methods.
-> Compare and contrast various soft computing frameworks.
-> Design distributed file systems.
-> Apply Stream data model.
-> Use Visualisation techniques
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.
REFERENCES:
1. Bill Franks, Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with advanced analystics, 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.
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