Data Anlytic's

A complete workshop on how to build and operate a data analytic unit

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Data Anlytic's by Mind Map: Data Anlytic's

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. 200 - Frameworks

2.1. 201 - Business Layer

2.1.1. 201.1 - How to frame a business problem

2.1.2. 201.2 - Key business problems

2.1.2.1. 201.2.1 - Risk Quantification

2.1.2.1.1. 201.2.1.1 - Market Risk

2.1.2.1.2. 201.2.1.2 - Credit Risk

2.1.2.1.3. 201.2.1.3 - Operational Risk

2.1.2.2. 201.2.2 - Valuation

2.1.2.2.1. DCF Models

2.1.2.2.2. Relative valuation models

2.1.2.2.3. Contingent claim models

2.1.2.2.4. Liquidation valuation models

2.1.2.2.5. Cost of carry models

2.1.2.3. 201.2.3 - Operational Sensitivity Analysis

2.1.2.4. 201.2.4 - Performance Optimisation

2.1.2.5. 201.2.5 - Portfolio Analytics

2.1.2.6. 201.2.6 - Scenario Analysis

2.1.2.7. 201.2.7 - Performance Dashboards

2.1.2.7.1. DuPont Dashboard

2.1.2.8. 201.2.8 - Cost Performance Models

2.1.2.8.1. ABC Costing

2.1.2.9. 201.2.9 - Forecasting

2.2. 202 - Database Layer

2.3. 203 - Modelling Tools

2.3.1. 203.1 - Excel and VBA

2.3.2. 203.2 - R-Project

2.4. 204 - Interface Layer

2.4.1. 204.1 - Capturing Data

2.4.2. 204.2 - Reporting Results

3. 300 - Data mining

3.1. 301 - Loading and Cleaning Data

3.2. 302 - Data Taxonomies

3.3. 303 - Data Stratification

3.4. 304 - Problems with data paucity

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

4.4. 404 - Charting