Data Science

Data Science

1. The interdisciplinary field that combines statistics, mathematics, and computing to extract insights from data.

   • Uses algorithms, machine learning, and statistical methods.
   • Helps in decision-making and predictions.
   • Involves collecting, processing, analyzing, and visualizing data.

2. Data Analytics

   • Subset of Data Science focused on analyzing raw data.
   • Identifies trends, patterns, and insights for business intelligence.
   • Uses tools like SQL, Tableau, and Python.

3. Big Data

   • Large and complex datasets that cannot be handled by traditional databases.
   • Characterized by the 3Vs: Volume, Velocity, Variety.
   • Requires technologies like Hadoop, Spark, and NoSQL databases.

4. Relation to Films

   • Margin Call
     • Highlights how financial analysts used data to predict the 2008 crisis within a bank.
     • Data Analytics played a crucial role in detecting risks in mortgage-backed securities.
   • The Big Short
     • Shows how analysts used unconventional data sources to foresee the market collapse.
     • Big Data methods were indirectly used to analyze housing market trends and loan risks.

5. CRISP-DM Methodology

   • 1. Business Understanding: Define objectives and success criteria.
   • 2. Data Understanding: Collect and explore data.
   • 3. Data Preparation: Clean and transform data.
   • 4. Modeling: Apply algorithms to find patterns.
   • 5. Evaluation: Validate the model’s performance.
   • 6. Deployment: Implement insights into real-world applications.