1. 100 - Why data anlytic's is important
1.1. 101 - The benefits of data analytics
1.2. 102 - Why separate analysis from transaction processing
1.3. 103 - Increasing the interface to models
1.4. 104 - Where has this worked
2. 300 - Data mining
2.1. 301 - Loading and Cleaning Data
2.2. 302 - Data Taxonomies
2.3. 303 - Data Stratification
2.4. 304 - Problems with data paucity
3. 200 - Frameworks
3.1. 201 - Business Layer
3.1.1. 201.1 - How to frame a business problem
3.1.2. 201.2 - Key business problems
3.1.2.1. 201.2.1 - Risk Quantification
3.1.2.1.1. 201.2.1.1 - Market Risk
3.1.2.1.2. 201.2.1.2 - Credit Risk
3.1.2.1.3. 201.2.1.3 - Operational Risk
3.1.2.2. 201.2.2 - Valuation
3.1.2.2.1. DCF Models
3.1.2.2.2. Relative valuation models
3.1.2.2.3. Contingent claim models
3.1.2.2.4. Liquidation valuation models
3.1.2.2.5. Cost of carry models
3.1.2.3. 201.2.3 - Operational Sensitivity Analysis
3.1.2.4. 201.2.4 - Performance Optimisation
3.1.2.5. 201.2.5 - Portfolio Analytics
3.1.2.6. 201.2.6 - Scenario Analysis
3.1.2.7. 201.2.7 - Performance Dashboards
3.1.2.7.1. DuPont Dashboard
3.1.2.8. 201.2.8 - Cost Performance Models
3.1.2.8.1. ABC Costing
3.1.2.9. 201.2.9 - Forecasting
3.2. 202 - Database Layer
3.3. 203 - Modelling Tools
3.3.1. 203.1 - Excel and VBA
3.3.2. 203.2 - R-Project
3.4. 204 - Interface Layer
3.4.1. 204.1 - Capturing Data
3.4.2. 204.2 - Reporting Results
4. 400 - Models
4.1. 401 - Distributions
4.1.1. 401.1 - Normal Distributions
4.1.1.1. 401.1.1 - Discrete Probability
4.1.1.2. 401.1.2 - Continuous Probability
4.1.1.3. 401.1.3 - Multivariate Distributions
4.1.1.4. 401.1.5 - Building distributions
4.1.1.4.1. 401.1.5.1 - Fitting
4.1.1.4.2. 401.1.5.2 - Building from assumptions
4.1.2. 401.2 - Extreme Value Theory
4.1.3. 401.3 - Multivariate data series
4.1.3.1. 404.3.1 - Volatility Modelling
4.1.3.2. 404.3.2 - Cyclical Modelling
4.1.3.3. 404.3.3 - Forecasting
4.2. 402 - Correlation & Dependency
4.2.1. 402.1 - Correlation 101
4.2.2. 402.2 - Concentration Risk
4.2.3. 403.3 - Cluster Modelling
4.3. 403 - Causal Modelling
4.3.1. 403.1 - Bayesian Trees
4.3.2. 403.2 - Random Forests
4.3.3. 404.3 - Partial Least Squares Path Modelling
4.3.4. 404.4 - Logistic Regression
4.3.5. 404.5 - Surveys