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Course Outline
Quick Overview
- Data Sources
- Data Management
- Recommender systems
- Target Marketing
Data Types
- 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
- k-Nearest Neighbours, k-means
- Ant colonies, birds flocking
Predictive Models
- Decision trees
- Support vector machine
- Naive Bayes classification
- Neural networks
- Markov Model
- Regression
- Ensemble methods
Return on Investment (ROI)
- Benefit-to-cost ratio
- Software costs
- Development costs
- Potential benefits
Building Models
- Data Preparation (MapReduce)
- Data cleansing
- Choosing 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
Familiarity with traditional data management and analysis methods such as SQL, data warehouses, business intelligence, OLAP, etc. A solid understanding of basic statistics and probability concepts (e.g., mean, variance, probability, conditional probability) is required.
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.
