Introduction

Importance of Economics for Data Scientists

• Venn Diagram Explanation
  • Three circles: Mathematics/Statistics/Probability, Computer Science, Domain Expertise
  • Domain Expertise: Knowledge in a particular field (e.g., Economics, Engineering, Medicine)
  • Fusion of these disciplines makes a good data scientist
• Economics as a Vital Domain
  • Quantitative discipline, complementary to Mathematics and Computer Science
  • Social Science aspect: Useful for studying societal problems
  • Foundation for business subjects like Finance and Accounting
  • Incorporates Statistics (Econometrics) for causal inference

Defining Economics

• Science of Decision Making
  • Everyday decisions (e.g., course enrollment)
  • Allocation of scarce resources to satisfy wants/desires
• Key Terms
  • Individual: Decision-making unit (can be a person, firm, or country)
  • Wants/Desires: Expectations or things we want to obtain
  • Scarcity: Limited resources (not just money, but also time, etc.)
  • Allocation: Distribution of goods and services
• Framework for Studying Societal and Business Problems
  • Applied course in constrained optimization
  • Balancing unlimited desires with limited resources

Managerial Economics

• Definition
  • Application of Economics to business problems
  • Study of how to direct scarce resources to achieve managerial goals
• Relationship to Economics
  • Subset of Microeconomics and Industrial Organization
  • Decision-making in firms, consumer behavior, and market interactions

Market Structures

• Different Types
  • Monopoly: One seller
  • Duopoly: Two sellers (e.g., Coca-Cola and Pepsi)
  • Oligopoly: Few sellers
  • Monopsony: One buyer
  • Perfectly Competitive Market: Idealized market for benchmarking
• Role of Government Rules and Regulations
• Importance of Information
  • Impacts behavior and decision-making

Platform Business Models

• Examples
  • Amazon, Uber
  • Platform for multiple sellers and buyers

Course Structure and Learning Style

• Problem-Solving Approach
  • Each chapter begins with a problem
  • Focus on tools to solve the problem
• Mathematical and Statistical Focus
  • Assumes basic knowledge of calculus and linear algebra
  • Mix of theory (mathematical models) and practical applications
• Problem Sets and Case Studies
  • Regular problem sets
  • Short case studies for discussion

Problem-Solving Approach: Key Questions

1. Who made the bad decision?
2. Did the decision-maker have enough information?
3. Did the decision-maker have the incentive?

Next Steps

• Upcoming topic: Mathematical modeling of economic problems