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is special issue gives the opportunity to know recent advances in the application of intelligent techniques to data-based optimization problems in scientific programming. Artificial intelligence is today supported for different powerful data science and optimization techniques. For instance, data science commonly relies on AI algorithms to efficiently solve classification, regression, and clustering problems. is fact is particularly interesting nowadays, when big data area gathers strength supplying huge amounts of data from many heterogeneous sources. On the other hand, complex optimization problems that cannot be tackled via traditional mathematical programming techniques are commonly solved with AI-based optimization approaches such as the metaheuristics. ese approaches provide optimal solutions avoiding consumption of many computational resources. Data science and AI-based optimization have also largely been used to solve problems related to scientific programming. Various examples are reported by the literature on task assignment in distributed/parallel systems, knowledge discovery, large-scale data mining, high-performance computing, big data, distributed/parallel search, text analysis/process/classification, and optimization for manufacturing, scheduling, and civil and financial engineering , among others. In this sense, this area provides a wide set of research lines and applications that deserves to be explored. is special issue presents nine original, high-quality articles, clearly focused on theoretical and practical aspects of the interaction between artificial intelligence and data science in scientific programming, including cutting-edge topics about optimization, machine learning, recommender systems, metaheuristics, classification, recognition, and real-world application cases. e first article in this special issue is entitled "Opti-mizing the Borrowing Limit and Interest Rate in P2P System: From Borrowers' Perspective" by Z. Li et al. is article shows a good example of how artificial intelligence algorithms can optimize some parameters involved in problems characterized by data flows. e work elaborates on the advantages of using a three-layer BP neural network algorithm to predict the borrowing limit and interest rate when individuals take advantage of P2P online service to borrow money. is approach provides a novel focus from borrowers to predict and optimize the borrowing limit and interest rate given the limited information. In addition, both parameters are optimized by means of an algorithmic proposal where the neural network and a genetic algorithm work together to solve both single-target and double-target programming optimization problems. e proposal is tested on real-world data to check its goodness as a high-accuracy prediction method. e second article is entitled "Leveraging

Data Science and AI-Based Optimization in Scientific Programming