Postgraduate Course: Statistical Learning in Banking (CMSE11651)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course is meticulously designed to impart a deep understanding of statistical learning methods and their application in the banking sector. The curriculum is focused on equipping students with the expertise required to analyse and interpret complex financial data. Through this course, learners will gain the ability to apply statistical techniques to derive meaningful insights, aiding in informed decision-making and strategic planning within the banking industry. |
| Course description |
This course is specifically designed to provide a profound understanding of statistical learning techniques, with a particular focus on their application in the banking sector. Throughout the course, participants will delve into the intricate world of statistical learning, mastering how to apply these methods effectively in banking. The course emphasises the strategic importance of statistical analysis in modern banking contexts, teaching students to leverage these skills for informed decision-making.
Students will become adept in using statistical tools to tackle real-world banking challenges, gaining practical experience in transforming complex financial data into meaningful banking insights. The course carefully balances advanced statistical learning concepts with their practical application, transcending the technical details of algorithms to highlight their utility in the banking industry.
Outline content
- Basic Statistical Learning Models
- Advanced Statistical Learning Models
- Model Assessment
- Deep Learning
Student Learning Experience
Students will cover the underlying principles of statistical learning techniques with a focus on understanding how and when to use each technique in a business setting.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Data-driven Business Insights (CMSE11648)
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2024/25, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
88 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% Project report (Individual) - Assesses all course Learning Outcomes |
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on assessment within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically evaluate the role and significance of statistical learning in the banking sector, understanding its impact on financial decision-making processes.
- Identify and elucidate a range of statistical methodologies specific to banking, including advanced statistical analysis, predictive modeling, and model assessment techniques.
- Gain proficiency in statistical modeling techniques, with a focus on their application and effectiveness in addressing banking-related challenges.
- Effectively communicate intricate statistical concepts and findings in a banking context, tailoring the communication to suit diverse stakeholder needs while maintaining clarity and accuracy.
- Develop a strategic analytical mindset, skilled at recognising, interpreting, and utilising statistical insights to solve complex issues in the banking industry.
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Reading List
Core text(s)
Geron, Aurelien. Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems / Aurelien Geron. Third edition. Beijing: O'Reilly, 2022. Print.
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2013) An Introduction to Statistical Learning with Applications in R. |
Additional Information
| Graduate Attributes and Skills |
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Knowledge and Understanding
After completing this course, students should be able to:
Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact. |
| Keywords | Banking,Statistical Learning,Risk Analytics |
Contacts
| Course organiser | Dr Stavros Stavroglou
Tel: (0131 6)51 1603
Email: Stavros.Stavroglou@ed.ac.uk |
Course secretary | |
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