Jingli Ren

For machine learning, both black-box and interpretable models are covered in this book, including neural network model, ensemble learning

This book offers a deep dive into the investment and business implications of climate change, uncovering both the emerging opportunities and pressing challenges. Through a powerful lens of big data analytics, it guides readers across four critical pillars of climate finance: climate data, indices, pricing, and reporting and engagement. The book equips readers with a comprehensive analytics toolkit by harnessing cutting-edge tools — large language models, generative AI, statistical inference in machine learning, optimization techniques, and advanced visualization methods. With a strong emphasis on practical application, it blends clear explanations, real-world case studies, and actionable insights to help investors, businesses, and financial professionals make informed decisions in a rapidly evolving field. Bridging the gap between theory and practice, this book stands as an indispensable resource for practitioners seeking to integrate data-driven strategies into climate finance and position themselves at the forefront of sustainable investing.

Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for data analysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.