UNIT 1:
Big Data Use Cases- Characteristics of Big Data Applications
Evolution of Big data - Best Practices for Big data Analytics
Map Reduce and YARN – Map Reduce Programming Model.
Perception and Quantification of Value
A General Overview of High-Performance Architecture
Map Reduce and YARN – Map Reduce Programming Model.
UNIT 2:
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 3:
Finding Association& finding similarity
Advanced Analytical Theory and Methods: Association Rules - Overview - Apriori Algorithm -
Evaluation of Candidate Rules
Knowledge Based Recommendation
Recommendation System: Collaborative Recommendation
Content Based Recommendation
Hybrid Recommendation Approaches.
UNIT 4:
Introduction to Streams Concepts – Stream Data Model and Architecture
Stream Computing, Sampling Data in a Stream
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,
UNIT 5:
NoSQL Databases: Schema-less Models‖: Increasing Flexibility for Data Manipulation
