Economics for Data Scientists
Theory, Tools, and Applications
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The book, Economics for Data Scientists: Theory, Tools, and Applications, serves as a comprehensive bridge between the rigor of economic theory and the power of data science tools. It is structured to help practitioners move beyond simple data prediction (a strength of machine learning) to genuine causal inference, understanding the "why" behind economic phenomena. The content establishes foundational economic concepts, such as Supply and Demand and Utility Theory, then transitions to applied techniques, covering everything from fundamental Regression Analysis (OLS) and handling complex Time S...
The book, Economics for Data Scientists: Theory, Tools, and Applications, serves as a comprehensive bridge between the rigor of economic theory and the power of data science tools. It is structured to help practitioners move beyond simple data prediction (a strength of machine learning) to genuine causal inference, understanding the "why" behind economic phenomena. The content establishes foundational economic concepts, such as Supply and Demand and Utility Theory, then transitions to applied techniques, covering everything from fundamental Regression Analysis (OLS) and handling complex Time Series data to advanced econometric methods for establishing Causality and addressing Endogeneity. Ultimately, the book equips the reader with a versatile toolkit to apply these blended concepts across real-world domains, including predicting Consumer Demand, optimizing Firm Behavior via supply chain analysis, and forecasting major Macroeconomic indicators, all while maintaining a focus on ethical data practices and the interpretive context of economic decision-making.
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