IT6006 Data Analytics Syllabus Notes Question Bank with answers

IT6006 Date Analytics Syllabus Notes Question Papers 2 Marks with Answers Question Bank with answers Anna University

Anna University IT6006 Data Analytics Notes Syllabus 2 marks with answers Part A Question Bank with answers

Anna University IT6006 Data Analytics Syllabus Notes 2 marks with the answer is provided below. IT6006 Notes Syllabus all 5 units notes are uploaded here. here IT6006 Data Analytics Syllabus notes download link is provided and students can download the IT6006 Syllabus and Lecture Notes and can make use of it.
Anna University Regulation 2013 Notes Syllabus Question Papers Question Banks - Rejinpaul

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IT6006 DATA ANALYTICS SYLLABUS REGULATION 2013
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.
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IT6006 Date Analytics Notes Syllabus Question Papers Question Bank
Anna University IT6006 Notes Syllabus Question Papers Question Bank 2 marks with answers
IT6006 Date Analytics Lecture Notes Subject Notes Anna University