DATA SCIENCE

DATA SCIENCE

1. Interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract insights and knowledge from data.

   • Statistics, computer science, mathematics, domain knowledge.
   • Transfrom raw data into useful knowledge for decision-making.

2. Subset of Data Science

   • Interpreting existing data to discover trends, patterns, and relationships.
   • Descriptive, Diagnostic, Predictive, Prescriptive.
     • Analysts uncover through risk analysis that their firm's models are broken and portfolios are toxic.
       • Investors analyze mortgage data and identify patterns other ignore, predicting the housing bubble collapse.

3. DATA ANALYTICS

4. Big Data

   • Extremely large and complex datasets that cannot be handled by traditional tools.
     • Volume, velocity, variety, veracity, value.
     • The firm deals with enormous financial datasets and stress simulations, which reveal catastrophic exposure.
       • Massive datasets of mortgages, rating, and defaults are analyzed to detect hidden systemic risks in the financial system.

5. CRISP-DM METHODOLOGY

   • Business Understanding: Define Objects.
     • Data Understanding: Explore Data.
       • Data Preparation: Clean and transform data.
         • Modeling: Build and test models.
           • Evaluation: Assess results.
             • Deployment: Act on insights.

6. Together, the films highlight both the power and the danger of relying on data, analytics, and models in high-stakes environments.