Data Analytics and Machine Learning for FinTech
| Code | School | Level | Credits | Semesters |
| BUSI4687 | Nottingham University Business School China | 4 | 20 | Spring China |
- Code
- BUSI4687
- School
- Nottingham University Business School China
- Level
- 4
- Credits
- 20
- Semesters
- Spring China
Summary
The module covers important concepts of (i) relational database design, (ii) data management and manipulation in Python, (iii) regression analysis (single, multiple, and logistic) (iv) decision trees, (v) naïve Bayes and K-NN, (vi) clustering, (vii) dimensionality reduction, (vii) support vector machines (SVM), and (ix) introduction to deep learning and neural network.
Target Students
Only Business School MSc Financial Technology students.
Classes
- One 2-hour lecture each week for 11 weeks
- One 2-hour computing each week for 11 weeks
Assessment
- 40% Group coursework: Group coursework (5,000-word report)
- 60% Exam 1 (2-hour): The exam paper consists of 3 parts: Parts A, B, and C. Part A consists of 10 questions all of which are compulsory. Parts B and C consist of 4 questions each. Students answer 2 out of 4 questions in each part.
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.
• Demonstrate effective written and oral communication
Conveners
- Dr Hang Zhou