Applied Data Science in FinTech: Models Tools and Case Studies By Juraj Hric Yiping Lin
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- 411
- File size
- 10.96 MB
- Format
- Digital PDF
- Course
- Education
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About this ebook
The textbook Applied Data Science in FinTech: Models, Tools, and Case Studies (2026) by Juraj Hric and Yiping Lin provides a comprehensive framework bridging data science and financial technology. [1, 2]
The book is structured into three distinct sections spanning 12 dedicated chapters that cover the following topics and core concepts: [1]
📊 Section 1: Data Science for FinTech
This section establishes the analytical foundation, guiding readers through the fundamentals of processing and interpreting financial information: [1]
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- Chapter 1: Data Science in FinTech – Introduction to the intersection of data science practices within modern financial systems.
- Chapter 2: Data Management for FinTech – Methods for handling, transforming, and organizing structural and alternative financial data.
- Chapter 3: Data Visualization – Design principles and programmatic tools to display financial trends and risk indicators.
- Chapter 4: Data Modeling in FinTech – Structural approaches to creating predictive and analytical frameworks using financial constraints. [1, 2]
💡 Section 2: Advanced Tools for Finance and FinTech
This segment transitions from fundamental concepts to specific technical execution tools, with a heavy emphasis on computational models and digital assets: [1, 2]
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- Chapter 5: Bitcoin and Tokenization – Cryptographic concepts, ledger ecosystems, and the digital tokenization of modern assets. [1]
- Chapter 6: Machine Learning Tools for Finance and FinTech – Deploying specific ML algorithms for financial forecasting, credit metrics, and optimization. [1]
- Chapter 7: Language Analytics for Finance and FinTech – Utilizing Natural Language Processing (NLP) to read regulatory documentation, financial filings, and text-based assets. [1]
- Chapter 8: Chatbots for Sentiment Analytics – Building conversational tools and mining social/market sentiments to extract trading or service insights. [1, 2]
➡️ Section 3: FinTech Applications
The final portion details real-world implementations, exploring niche verticals within the tech-finance landscape: [1, 2]
- Chapter 9: FinTech Application: AdviceTech – Data solutions engineered for computerized financial planning, wealth management, and robo-advisory systems.
- Chapter 10: FinTech Application: AgTech – The intersection of finance and agricultural technology, including predictive modeling for commodity pricing and sustainable supply-chain loans.
- Chapter 11: FinTech Application: PropTech – Data analytics applied to property and real estate technology, focusing on pricing models and spatial data risks.
- Chapter 12: Data Frontiers in FinTech – A concluding analysis of emerging data sets, ethical handling boundaries, and next-generation industry architectures. [1, 2]
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