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Course Outline
Quick Overview
- Data Sources
- Data Stewardship
- Recommender systems
- Target Marketing
Datatypes
- Structured vs unstructured data
- Static vs streamed data
- 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
- k-Groups, k-means, and nearest neighbours
- Bio-inspired models (ant colonies, birds flocking)
Predictive Models
- Decision trees
- Support vector machines
- Naive Bayes classification
- Neural networks
- Markov Models
- Regression
- Ensemble methods
ROI
- Benefit-to-cost ratio
- Software costs
- Development costs
- Potential benefits
Building Models
- Data Preparation (MapReduce)
- Data cleansing
- Selecting appropriate methods
- Model development
- Model testing
- Model evaluation
- Model deployment and integration
Overview of Open Source and commercial software
- Selection of R-project packages
- Python libraries
- Hadoop and Mahout
- Selected Apache projects related to Big Data and Analytics
- Selected commercial solutions
- Integration with existing software and data sources
Requirements
A solid understanding of traditional data management and analysis methods, such as SQL, data warehouses, business intelligence, OLAP, and similar concepts, is required. Familiarity with basic statistics and probability theory (including mean, variance, probability, conditional probability, etc.) is also necessary.
21 Hours
Testimonials (2)
The content, as I found it very interesting and think it would help me in my final year at University.
Krishan - NBrown Group
Course - From Data to Decision with Big Data and Predictive Analytics
Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.
