IT6006 Data Analytics Syllabus Semester VII Elective IT BTECH Anna University-Regulation-2013

NotesKhan




IT6006                                                  DATA ANALYTICS                                                        L T P C
3 0 0 3
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

UNIT I             INTRODUCTION TO BIG DATA                                                                                    8
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                                                                                                        12
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                                                                                             8
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                                                                9
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                                                                         8
MapReduce – Hadoop, Hive, MapR – Sharding – NoSQL Databases - S3 - Hadoop Distributed file systems – Visualizations - Visual data analysis techniques, interaction techniques; Systems and applications:


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

TOTAL: 45 PERIODS


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.