# Introduction

The emergence of big data has had a transformative effect across multiple domains, including economics and finance. Traditional forecasting models, while effective, are limited in handling unstructured data and non-linear relationships. Data science technologies, especially machine learning algorithms, offer a potential solution to these limitations. However, interpretability remains a challenge in fully integrating these algorithms into economic decision-making. This lecture delves into these facets, focusing on the implications of integrating data science in economic forecasting and policy analysis.

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## Sections

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