This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models.

中文翻译：

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经济学中的数据科学：高级机器学习和深度学习方法的全面回顾

本文对新兴经济应用中数据科学的最新进展进行了全面的最新研究。在四个单独类别的深度学习模型，混合深度学习模型，混合机器学习和集成模型中，对新颖的数据科学方法进行了分析。应用领域包括从股票市场，市场营销和电子商务到公司银行业务和加密货币的广泛而广泛的经济学研究。棱镜法是一种系统的文献综述方法，用于确保调查的质量。研究结果表明，趋势跟随混合模型的发展，而混合模型的表现优于其他学习算法。可以预见的是，这种趋势将趋向于复杂的混合深度学习模型的发展。