Singapore University of Social Sciences

Introduction to Financial Data Science

Introduction to Financial Data Science (FIN552)

Synopsis

FIN552 Introduction to Financial Data Science provides a systematic approach to leveraging financial big data for financial decision-making purposes. As financial institutions increasingly recognise the importance of collecting and processing large datasets, the demand for professionals skilled in handling these data has surged. By utilising affordable computational resources, vast amounts of data can be analysed to uncover hidden patterns and relationships that were previously undetectable. This course addresses the urgent need for future financial industry participants to organise, handle, and manipulate real-time financial data, enabling them to respond swiftly to changing market conditions. In this course, you will explore the application and implementation of data science concepts in finance. The course covers data processing techniques and how to convert data into formats suitable for statistical and machine learning models. Through real-world case studies, from established fields like asset management to emerging areas such as cryptocurrencies, you will gain practical insights into applying data science concepts in various financial context.

Level: 5
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER

Topics

  • Introduction to Python and Python tools
  • Native Data and Structured Data Types in Python
  • Control Sequences
  • User Defined Functions
  • Best Programming Practices
  • Documentation Using Inline Comments and Markdown
  • Introduction to Python Modules – Numpy and Matplotlib
  • Introduction to Financial Data Sources
  • Large-Scale Financial Calculations
  • Real-World Financial Data Handling and Wrangling
  • Case Studies on Financial Data Science
  • Analysis Result Communication and Visualisation

Learning Outcome

  • Organise and prepare large datasets for analysis using Python
  • Design suitable program flow required for real-world financial applications using Python
  • Assemble and use available Python modules for rapid prototyping and development
  • Formulate and propose suitable metric and visualisation for result communication
  • Apply good programming practices in implementation
  • Create documentation for source code management
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