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Course Outline
Quick Overview
- Data Sources
- Minding Data
- 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-Groups, k-Means, and the Nearest Neighbours
- Ant Colonies and Bird Flocking
Predictive Models
- Decision Trees
- Support Vector Machines
- Naive Bayes Classification
- Neural Networks
- Markov Models
- Regression
- Ensemble Methods
ROI
- Benefit/Cost Ratio
- Software Costs
- Development Costs
- Potential Benefits
Building Models
- Data Preparation (MapReduce)
- Data Cleansing
- Choosing Methods
- Developing the Model
- Testing the Model
- 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. Additionally, a foundational knowledge of basic statistics and probability (including mean, variance, probability, conditional probability, etc.) is essential.
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.
