Anna University 2013 Regulation - IT6006 Data Analytics - Syllabus - Download
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:
Anna University 2013 Regulation - IT6006 Data Analytics - Syllabus - Download
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:
Anna University 2013 Regulation - IT6006 Data Analytics - Syllabus - Download
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