Muutke küpsiste eelistusi

Applied Data Science in FinTech: Models, Tools, and Case Studies [Pehme köide]

  • Formaat: Paperback / softback, 388 pages, kõrgus x laius: 246x174 mm, kaal: 760 g, 154 Halftones, color; 154 Illustrations, color
  • Ilmumisaeg: 11-Mar-2026
  • Kirjastus: Routledge
  • ISBN-10: 1032762578
  • ISBN-13: 9781032762579
Teised raamatud teemal:
  • Formaat: Paperback / softback, 388 pages, kõrgus x laius: 246x174 mm, kaal: 760 g, 154 Halftones, color; 154 Illustrations, color
  • Ilmumisaeg: 11-Mar-2026
  • Kirjastus: Routledge
  • ISBN-10: 1032762578
  • ISBN-13: 9781032762579
Teised raamatud teemal:

This text 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.



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.

Section
1. Data Science for FinTech

Chapter
1. Data Science in FinTech

Chapter
2. Data Management for FinTech

Chapter
3. Data Visualization

Chapter
4. Data Modeling in FinTech

Section
2. Advanced Tools for Finance and FinTech

Chapter
5. Bitcoin and Tokenization

Chapter
6. Machine Learning Tools for Finance and FinTech

Chapter
7. Language Analytics for Finance and FinTech

Chapter
8. Chatbots for Sentiment Analytics

Section
3. FinTech Applications

Chapter
9. FinTech Application: AdviceTech

Chapter
10. FinTech Application: AgTech

Chapter
11. FinTech Application: PropTech

Chapter
12. Data Frontiers in FinTech
Juraj Hric is Co-Founder and Chief Executive Officer at Zyanza Technologies. He is a course convenor for banking, finance and financial technology modules at the University of New South Wales, Australia.

Yiping Lin is Co-Founder of Alt Data Tech, a leading provider of alternative data. He is also the director of undergraduate and postgraduate FinTech programs at the University of New South Wales, Australia.