Applied Data Science In Fintech
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Applied Data Science in FinTech
This textbook offers a comprehensive introduction to data science and financial technology, with a focus on advanced tools, data modeling, and their applications in FinTech. Adopting an inquiry-based approach, it integrates detailed case studies, clear definitions of financial terms, and practical examples to guide readers through core concepts and methods. Step-by-step illustrations demonstrate how programs are developed, making the material accessible for students. Dedicated chapters explore cutting-edge applications such as AdviceTech, AgTech, PropTech, chatbots, and sentiment analytics. To support hands-on learning, the book also provides sample code and data sets, enabling readers to experiment, practice, and ultimately design their own programs. Designed for those with a basic foundation in programming, this book is an ideal companion for applying data science techniques to financial and technological contexts. It is particularly valuable for postgraduate and advanced students in FinTech, Business Analytics, and Data Science programs.
Applied Data Science in Fintech
This textbook offers a comprehensive introduction to data science and financial technology, with a focus on advanced tools, data modeling, and their applications in FinTech. Adopting an inquiry-based approach, it integrates detailed case studies, clear definitions of financial terms, and practical examples to guide readers through core concepts and methods. Step-by-step illustrations demonstrate how programs are developed, making the material accessible for students. Dedicated chapters explore cutting-edge applications such as AdviceTech, AgTech, PropTech, chatbots, and sentiment analytics. To support hands-on learning, the book also provides sample code and datasets, enabling readers to experiment, practice, and ultimately design their own programs. Designed for those with a basic foundation in programming, this book is an ideal companion for applying data science techniques to financial and technological contexts. It is particularly valuable for postgraduate and advanced students in FinTech, Business Analytics, and Data Science programs.
Applied Data Science
This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science:first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.