CS8091 BIG DATA analytics

CS8091 BIG DATA analytics 

Stuff Sector
      Prepared by  
      Santhosh (Admin) 

*UNIT I                 

1. Characteristics of Big Data Applications 

2. Overview of High-Performance Architecture - HDFS

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 3.Evolution,Best Practices for  Big data and it's characteristics

*UNIT II*                    

1.K-means, Determining the Number of Clusters , diagnostics.         Don't share as screenshot -Stuff sector

2.Decision Tree Algorithms 

3. Naïve Bayes, Bayes‘ Theorem 

*UNIT III*                                         

1.Apriori Algorithm,Evaluation of Candidate Rules**Don't share as screenshot -Stuff sector

2.Collaborative, content based,hybrid  Recommendation

*UNIT IV*                 

1. Stream Data Model and Architecture and Computing

 2.Decaying Window,(RTAP) apps  

3.Stock Market Predictions. ,Using Graph Analytics for Big Data: Graph Analytics** may be part C 

*UNIT V*         

1. Increasing Flexibility for Data Manipulation

 2.Graph Databases Hive, Sharding, Hbase  Analyzing big data with twitter Don't share as screenshot -Stuff sector

  3..Basic Data Analytic Methods using R.

SYllabuS

*UNIT I INTRODUCTION TO BIG DATA*

Evolution of Big data - Best Practices for Big data Analytics - Big data characteristics - Validating - The Promotion of the Value of Big Data - Big Data Use Cases- Characteristics of Big Data Applications - Perception and Quantification of Value -Understanding Big Data Storage - A General Overview of High-Performance Architecture - HDFS - MapReduce and YARN - Map Reduce Programming Model

*UNIT II* *CLUSTERING AND CLASSIFICATION*

Advanced Analytical Theory and Methods: Overview of Clustering - K-means - Use Cases - Overview of the Method - Determining the Number of Clusters - Diagnostics - Reasons to Choose and Cautions .- Classification: Decision Trees - Overview of a Decision Tree - The General Algorithm - Decision Tree Algorithms - Evaluating a Decision Tree - Decision Trees in R - Naïve Bayes - Bayes‘ Theorem - Naïve Bayes Classifier.

*UNIT III* *ASSOCIATION AND RECOMMENDATION SYSTEM*                             

Advanced Analytical Theory and Methods: Association Rules - Overview - Apriori Algorithm - Evaluation of Candidate Rules - Applications of Association Rules - Finding Association& finding similarity - Recommendation System: Collaborative Recommendation- Content Based Recommendation - Knowledge Based Recommendation- Hybrid Recommendation Approaches.

*UNIT IV* *STREAM MEMORY*

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 – Real time Analytics Platform(RTAP) applications - Case Studies - Real Time Sentiment Analysis, Stock Market Predictions. Using Graph Analytics for Big Data: Graph Analytics

*UNIT V* *NOSQL DATA MANAGEMENT FOR BIG DATA AND VISUALIZATION*    

NoSQL Databases : Schema-less Models‖: Increasing Flexibility for Data Manipulation-Key Value Stores- Document Stores - Tabular Stores - Object Data Stores - Graph Databases Hive - Sharding –- Hbase – Analyzing big data with twitter - Big data for E-Commerce Big data for blogs - Review of Basic Data Analytic Methods using R.