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Course Outline
Quick Overview
- Data Sources
- Data Management
- Recommender Systems
- Targeted Marketing
Data Types
- Structured versus Unstructured Data
- Static versus Streamed Data
- Attitudinal, Behavioral, and Demographic Data
- Data-Driven versus User-Driven Analytics
- Data Validity
- Volume, Velocity, and Variety of Data
Models
- Model Construction
- Statistical Models
- Machine Learning
Data Classification
- Clustering Techniques
- k-Groups, k-means, and Nearest Neighbors
- Swarm Intelligence (e.g., Ant Colonies, Bird Flocking)
Predictive Models
- Decision Trees
- Support Vector Machines
- Naive Bayes Classification
- Neural Networks
- Markov Models
- Regression Analysis
- Ensemble Methods
Return on Investment (ROI)
- Benefit-Cost Ratio
- Software Costs
- Development Costs
- Potential Benefits
Building Models
- Data Preparation (MapReduce)
- Data Cleansing
- Method Selection
- 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 for Big Data and Analytics
- Selected Commercial Solutions
- Integration with Existing Software and Data Sources
Requirements
Participants should possess a foundational understanding of traditional data management and analysis methods, including SQL, data warehouses, business intelligence, and OLAP. Familiarity with basic statistics and probability concepts, such as mean, variance, probability, and conditional probability, is also 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.
