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Routledge – Applied Data Science in FinTech 2026

Updated August 10, 2026 47 MB
Routledge – Applied Data Science in FinTech 2026

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Description

Applied Data Science in FinTech is a comprehensive guide to the intersection of data science and financial technology (FinTech), exploring advanced tools and data modeling with a question-driven approach. The author has strived to make complex concepts tangible for students and professionals in the field by integrating detailed case studies and clear definitions of financial terms.

The book provides a step-by-step guide to developing applications, with separate chapters dedicated to emerging applications such as AdviceTech, Agritech and Real Estate, Chatbots, and Sentiment Analysis. This book is an ideal resource for those with a programming background to design custom applications in the financial world.

Book Features

  • Step-by-step training on developing financial software using data science.
  • Providing sample codes and real datasets for practical practice.
  • Specialized review of the concepts of blockchain, token economy, and cybersecurity in fintech.
  • Focus on deep learning and natural language processing (NLP) models in financial markets.
  • Suitable for graduate students in business analytics and data science.

Book specifications

  • Publisher: Routledge
  • Instructor/Author: Juraj Hric
  • Number of pages: 691
  • Number of seasons: 3
  • Format: PDF

Headlines

The book Applied Data Science in FinTech is a comprehensive guide to the intersection of data science and financial technology (FinTech).

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The book Applied Data Science in FinTech is a comprehensive guide to the intersection of data science and financial technology (FinTech).

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47 MB

What is included

  • Step-by-step training on developing financial software using data science.
  • Providing sample codes and real datasets for practical practice.
  • Specialized review of the concepts of blockchain, token economy, and cybersecurity in fintech.
  • Focus on deep learning and natural language processing (NLP) models in financial markets.
  • Suitable for graduate students in business analytics and data science.