Course Outline

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

Requirements

Understanding of traditional data management and analysis methods like SQL, data warehouses, business intelligence, OLAP, etc... Understanding of basic statistics and probability (mean, variance, probability, conditional probability, etc....)
  21 Hours
 

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Testimonials

the scope of material

Maciej Jonczyk

systematizing knowledge in the field of ML

Orange Polska

Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.

Jamie Martin-Royle - NBrown Group

The content, as I found it very interesting and think it would help me in my final year at University.

Krishan Mistry - NBrown Group

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