Big Data Analytics
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BIG DATA ANALYTICS L T P C 2 0 0 2 UNIT I INTRODUCTION TO BIG DATA 6 Introduction to BigData Platform – Traits of Big data -Challenges of Conventional Systems - Evolution f !nalytic Scalability - !nalytic Processes and Tools - !nalysis vs "e#orting - $ Tools UNIT II DATA ANALYSIS 6 "egression $odeling - $ultivariate !nalysis - Bayesian $odeling - Inference and Bayesian %et of Time Series( )inear Systems !nalysis - %onlinear Dynamics - *u++y )ogic( E,tracting *u++y Data - *u++y Decision Trees - Stochastic Search $ethods UNIT IIIMINING DATA STREAMS 6 Introduction To Streams Conce#ts – Stream Data $odel and !rchitecture - Stream Com#uting - S a Stream – *iltering Streams –"eal time !nalytics Platform."T!P/ !##lications - Case Study( Predictions UNIT IV FREQUENT ITEMSETS AND CLUSTERING 6 $ining *re0uent Itemsets - $ar'et Based $odel – !#riori !lgorithm – 1andling )arge Data Sets $emory – )imited Pass !lgorithm - Clustering Techni0ues – 1ierarchical – 2-$eans – *re0uent Clustering $ethods – Clustering in %on-Euclidean S#ace UNIT V FRAMEWORKS 6 $a#"educe – 1adoo#3 1ive3 $a#"– %oS4) Databases - S5 - 1adoo# Distributed *ile Systems -6nderstanding of emerging trends and technologies-Industry challenges and a##l Total No. o !"#$o%&' (0 REFERENCE BOOKS' 7 $ichael Berthold3 David 8 1and3 9Intelligent Data !nalysis:3 S#ringer3 ;<<= ; !nand"a>araman and 8effrey David 6llman3 9$ining of $assive Datasets:3 Cambridge 6niversi 5 Bill *ran's3 9Taming the Big Data Tidal Wave( *inding ##ortunities in 1uge Data Streams !nalytics:3 8ohn Wiley ? sons3 ;<7; @ Alenn 8 $yatt3 9$a'ing Sense of Data:3 8ohn Wiley ? Sons3 ;<<= Pete Warden3 9Big Data Alossary:3 "eilly3 ;<77 8ia&ei 1an3 $icheline2amber 9Data $ining Conce#ts and Techni0ues:3 Second Edition3
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Transcript of Big Data Analytics
BIG DATA ANALYTICS L T P C 2 0 0 2UNIT I INTRODUCTION TO BIG DATA
6Introduction to BigData Platform Traits of Big data -Challenges of Conventional Systems - Web Data Evolution Of Analytic Scalability - Analytic Processes and Tools - Analysis vs Reporting - Modern Data Analytic Tools
UNIT II DATA ANALYSIS
6Regression Modeling - Multivariate Analysis - Bayesian Modeling - Inference and Bayesian Networks - Analysis of Time Series: Linear Systems Analysis - Nonlinear Dynamics - Fuzzy Logic: Extracting Fuzzy Models from Data - Fuzzy Decision Trees - Stochastic Search Methods.
UNIT III MINING DATA STREAMS
6Introduction To Streams Concepts Stream Data Model and Architecture - Stream Computing - Sampling Data in a Stream Filtering Streams Real time Analytics Platform(RTAP) Applications - Case Study: Stock Market Predictions.
UNIT IV FREQUENT ITEMSETS AND CLUSTERING
6 Mining Frequent Itemsets - Market Based Model Apriori Algorithm Handling Large Data Sets in Main Memory Limited Pass Algorithm - Clustering Techniques Hierarchical K-Means Frequent Pattern based Clustering Methods Clustering in Non-Euclidean Space .
UNIT V FRAMEWORKS
6 MapReduce Hadoop, Hive, MapR NoSQL Databases - S3 - Hadoop Distributed
File Systems -Understanding of emerging trends and technologies-Industry challenges and application of Analytics
Total No. of periods: 30REFERENCE BOOKS:
1. Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer, 2007.
2. AnandRajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press, 2012.
3. Bill Franks, Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics, John Wiley & sons, 2012.
4. Glenn J. Myatt, Making Sense of Data, John Wiley & Sons, 2007
5. Pete Warden, Big Data Glossary, OReilly, 2011.
6. Jiawei Han, MichelineKamber Data Mining Concepts and Techniques, Second Edition,
