Data Analytics and Machine Learning for FinTech
| Code | School | Level | Credits | Semesters |
| BUSI4620 | Business | 4 | 20 | Spring UK |
- Code
- BUSI4620
- School
- Business
- Level
- 4
- Credits
- 20
- Semesters
- Spring UK
Summary
This module provides a comprehensive exploration of data science to equip students with a profound understanding of key concepts and techniques. The module encompasses fundamental topics in data management and manipulation in Python and then students will delve into dimensionality reduction techniques to extract essential features. Regression analysis, covering single, multiple, and logistic regression, forms a cornerstone, providing students with a foundation for predictive modelling with supervised learning. The discussion extends to a probabilistic approach, naive Bayes, and a non-parametric algorithm, K-NN. Furthermore, the module discusses advanced algorithms, including Support Vector Machines (SVM) and decision trees, elucidating their practical implications. A segment on clustering methods sheds light on unsupervised learning, demonstrating how to identify patterns within data. The module also introduces deep learning and neural networks, offering a foundation for the cutting-edge advancements in artificial intelligence. Additionally, students will learn relational database design, a skill for managing data in real-world applications. Through a blend of theoretical insights and hands-on exercises, this module aims to empower students with the analytical prowess required to navigate the landscape of data analytics.
Target Students
Available for MSc Financial Technology students and MSc Exchange students.
Classes
- One 2-hour lecture each week for 11 weeks
- One 1-hour computing each week for 11 weeks
This module is taught through a combination of computing sessions and lectures.
Assessment
- 40% Coursework 1: 3,000 words. Reassessment method: 100% coursework with a 4,000 word limit.
- 60% Coursework 2: 3,000 words. Reassessment method: 100% coursework with a 4,000 word limit.
Assessed by end of spring semester
Educational Aims
This module aims to introduce students to methods of manipulating, managing, and analysing big data. It also introduces students to important concepts of supervised and unsupervised machine learning.Learning Outcomes
Knowledge and understanding:
On successful completion of this module, students should be able to
• Manage, manipulate, and analyse different data types and data structure.
• Discuss the fundamentals of machine learning.
• Describe the fundamentals of data mining.
• Discuss fundamentals of pattern recognition.
Intellectual Skills:
This module develops:
• Evaluate and identify appropriate machine learning techniques to analyse economic and financial data.
Professional Practical Skills:
This module develops:
• Apply a range of programming skills to manage, manipulate, and analyse economic and financial data using appropriate machine learning techniques.
Transferable (key) Skills:
This module develops:
• Manage and interpret numerical and statistical data.
• Manage independent study and demonstrate effective planning and time-management skills.
• Critically evaluate research and information from various sources.