Singapore University of Social Sciences

Fintech Data Revolution: Big Data, Data Science, and Structures 金融科技数据革命:大数据、数据科学与数据结构

Fintech Data Revolution: Big Data, Data Science, and Structures 金融科技数据革命:大数据、数据科学与数据结构 (FTH524)

Synopsis

FTH524 Fintech Data Revolution: Big Data, Data Science, and Structures constructs an indepth exploration of big data, integrating the critical elements of data science such as mathematics, statistics, programming, and domain expertise. This course introduces essential terminologies and examines various data structures and algorithms. It emphasises data science as a multidisciplinary field that employs scientific methods, processes, algorithms, and systems to extract and synthesise knowledge from diverse data types. Students will learn to apply these methods to solve problems through programming, enhancing their ability to search, organise, and process large datasets efficiently, formulating and implementing algorithms to create data-driven fintech solutions. FTH524 金融科技数据革命:大数据、数据科学与数据结构整合了数学、统计学、编程和 领域专业知识等数据科学的关键要素,对大数据进行了深入阐述。本课程介绍了数据科学 的基本术语以及多种数据结构和算法,并强调数据科学是一个多学科领域,采用科学方法、 过程、算法和系统从不同的数据类型中提取和综合知识。学生将学习应用这些方法通过编 程来解决问题,提高有效搜索、组织和处理大型数据集的能力,制定和实施算法以创建数 据驱动的金融科技解决方案。

Level: 5
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

  • Big Data in Fintech 金融科技中的大数据
  • Fundamentals of Data Science: Mathematics, Statistics, and Programming 数据科学基础:数 学、统计学和编程
  • Data Structures and Algorithms for Fintech Applications 金融科技应用的数据结构和算法
  • Scientific Methods in Data Science for Financial Analysis 金融分析的数据科学方法
  • Programming for Data Processing and Analysis in Fintech 金融科技数据处理和分析编程
  • Applying Data Science to Solve Fintech Problems 应用数据科学解决金融科技问题
  • Data Types and Structures in Financial Datasets 金融数据类型和结构
  • Algorithm Development for Big Data Solutions in Finance 金融大数据解决方案算法开发
  • Data Synthesis and Knowledge Extraction Techniques 数据合成和知识提取技术
  • Efficient Data Searching and Organising Strategies 高效的数据搜索和组织策略
  • Innovative Approaches to Data-Driven Problem Solving in Fintech 金融科技中数据驱动问 题解决的创新方法
  • Emerging Trends and Future Directions in Fintech Data Science 金融科技数据科学的新兴趋 势和未来方向

Learning Outcome

  • Appraise various data structures and their applicability in solving complex fintech problems 评估各种数据结构及其在解决复杂金融科技问题中的适用性
  • Evaluate the key components of data science, including mathematics, statistics, and programming, in the context of big data 在大数据背景下评估数据科学的关键组成部分, 包括数学、统计学和编程
  • Critique the multidisciplinary approach of data science in extracting and synthesizing knowledge 评估数据科学提取和综合知识的多学科方法
  • Construct data-driven solutions by applying scientific methods and processes to diverse data types 将科学方法和流程应用于不同的数据类型来构建数据驱动的解决方案
  • Formulate and implement algorithms for efficient data searching, organising, and processing in large datasets 制定并实现在大型数据集中进行高效数据搜索、组织和处 理数据的算法
  • Design and develop programming solutions to address specific challenges in the fintech industry using big data and data science techniques 利用大数据和数据科学技术设计和 开发编程解决方案,以应对金融科技行业的具体挑战
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