МЕТОДИ ТА МОДЕЛІ DATA SCIENCE В СУЧАСНІЙ ЕКОНОМІЦІ

Abstract

In contemporary economics, data science models play a crucial role in analyzing complex relationships, predicting economic trends, and informing policy decisions. This article reviews the most commonly used data science models in economics, including econometric models like linear and logistic regression, Probit and Tobit models, time series analysis models such as ARIMA and Vector Autoregression (VAR), and panel data analysis methods like fixed and random effects models and Difference-in-Differences (DiD). Additionally, it explores machine learning algorithms, clustering and classification techniques, dimensionality reduction methods, Bayesian methods, and natural language processing (NLP) applications. The article highlights their purposes, applications, and relevant works, emphasizing the strengths and limitations of each model. It also discusses the impact of these models across various sectors, including finance, retail, energy, and healthcare. This comprehensive overview underscores the importance of aligning data science models with business objectives, ensuring data quality, investing in scalable technologies, fostering a data-driven culture, and addressing ethical considerations. The article concludes with future research directions, such as advanced neural network architectures, large language models, generative AI models, hybrid models, and the need for interpretable and ethical AI applications in economics. The importance of this topic lies in the transformative potential of data science models to enhance economic analysis and decision-making. By leveraging advanced data science techniques, economists can gain deeper insights into complex economic phenomena, improve forecasting accuracy, and develop more effective policies. As data-driven approaches continue to evolve, they provide powerful tools for addressing critical economic challenges, driving innovation, and fostering sustainable growth across various sectors.