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• Quick Overview
  Data Sources
  Minding Data
  Recommender systems
  Target Marketing
  Datatypes
  Structured vs unstructured
  Static vs streamed
  Attitudinal, behavioural and demographic data
  Data-driven vs user-driven analytics
  data validity
  Volume, velocity and variety of data
  Models
  Building models
  Statistical Models
  Machine learning
  Data Classification
  Clustering
  kGroups, k-means, the nearest neighbours
  Ant colonies, birds flocking
  Predictive Models
  Decision trees
  Support vector machine
  Naive Bayes classification
  Neural networks
  Markov Model
  Regression
  Ensemble methods
  ROI
  Benefit/Cost ratio
  Cost of software
  Cost of development
  Potential benefits
  Building Models
  Data Preparation (MapReduce)
  Data cleansing
  Choosing methods
  Developing model
  Testing Model
  Model evaluation
  Model deployment and integration
  Overview of Open Source and commercial software
  Selection of R-project package
  Python libraries
  Hadoop and Mahout
  Selected Apache projects related to Big Data and Analytics
  Selected commercial solution
  Integration with existing software and data sources