Coding Analytics for Accounting and Finance
| Module title | Coding Analytics for Accounting and Finance |
|---|---|
| Module code | BEAM079 |
| Academic year | 2023/4 |
| Credits | 30 |
| Module staff | Dr Anthony Wood (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 2 |
| Number students taking module (anticipated) | 40 |
|---|
Module description
Using Python as its core language, students will engage in an intensive program of coding and analytics using up-to-date software and methodology. Students are not required to have any previous coding experience but the module would be suited to students with a good level of analysis, statistics, and problem-solving abilities. By the end of this module students will have undertaken several projects and challenges related to the Accounting and Finance literature, with the skills and knowledge acquired being easily transferable to other disciplines and pathways. Python will be used to perform complex analytics on various contemporary Accounting and Finance research areas such as fraud detection, social network analysis and textual/sentiment analysis. By the end of this course students will have a good understanding of up-to-date research methods and be able to use software and technologies in order to independently tackle complex problems. With the fast evolution of AI, analytics and data science within the workplace, this module is an ideal way to equip students with the tools and knowledge required to stand out in an increasingly competitive employment market.
Module aims - intentions of the module
The aim of this module is to enhance data analytics and digital competencies for students within the Accounting and Finance curriculum and beyond. Students will begin by being introduced to the Python programming language during a one-day course where they will learn the basic functionality and core aspects of the program through multiple tasks and challenges. The remainder of the module builds on this core knowledge and understanding of Python and applies it in a more direct way to statistical data analysis and Accounting and Finance research. The latter half of the module focuses on four distinct contemporary topics which have been selected based on special interest articles called for by journals such as Accounting Horizons and Research Policy. These topics will be complemented by discussions of relevant academic literature and will form the basis of the individual research project. By the end of the module, students will have a deep understanding of python and its application and potential within statistics, data analysis (both numerical and textual), and research. Students will have demonstrated the ability to use the up-to-date tools and resources provided in order to undertake a sustained program of independent research and knowledge acquisition in order to solve complex problems; and thereby better preparing themselves for a rapidly changing and increasingly technology-based workplace.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Recognise the usage and context of contemporary accounting and finance research
- 2. Distinguish between quantitative and qualitative research processes and techniques
- 3. Recognise the theory and rationale behind various analysis and research methods
- 4. Identify practical issues and limitations surrounding research projects
- 5. Utilise investigatory skills in order to find solutions to complex logic problems
- 6. Identify, critique and develop research papers and research methodology
- 7. Interpret and apply data sources, statistics, and relevant software
- 8. Use Python to perform and present various statistical analyses using numerical and textual data
- 9. Use coding techniques to share original content and insight through visualisation
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 10. Critically evaluate techniques and methodology used within accounting research
- 11. Identify research questions and opportunities for new and exciting disciplinary research
- 12. Bring together, summarize and critique a rounded body of independently sourced material
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 13. Demonstrate key skills in coding and analytical practices including data handling
- 14. Demonstrate effective use of learning resources and independent research skills
- 15. Pursue and deliver on a sustained program of individual work successfully
- 16. Complete multistage tasks within a defined period whilst assisted by supervision
- 17. Identify the latest developments in technology and programming
- 18. Use contemporary technology to tackle complex problems
Syllabus plan
The module syllabus will include the following topics:
- Introduction to Python
- Handling financial data with the ‘pandas’ software library
- Statistical analysis for accounting research
- Visualisation and report presentation
- Textual analysis in auditing and accounting
- Business and social network analysis
- Fraud detection in corporate accounts
- Web and social media scraping for accounting research
- Machine learning to enhance empirical accounting methods
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 40 | 260 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activity | 40 | Interactive workshop / study sessions (One 8-hour session plus eight 4-hour sessions) |
| Guided Independent Study | 260 | Private study with remote support |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Study sessions | 1 x 8 hours plus 8 x 4 hours | 1-18 | Verbal, ELE |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Coursework - Individual research project | 100 | 7500 words (this covers everything submitted i.e. including references, tables, graphs, contents pages, acknowledgements and any appendices). In addition, students will be required to submit (in a seperate document) all code which was used to prepare and present the final research project. Both documents are to be uploaded electronically via ELE. | 1-18 | Written feedback |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Individual Research Project (100%) (7500 words) | Individual Research Project (100%) (7500 words) | 1-18 | 3 months after original submission date |
Re-assessment notes
Notes will be provided on a feedback form. A ‘to-do’ list provided to each student eligible to resubmit.
The word limit is 7500 (under the same regulations as the original submission). Students are required to submit an electronic copy of the research project and any code within 3 months of the original submission date. Re-assessment will be 100% for this module and capped at 50%.
Indicative learning resources - Basic reading
Matthes, E. (2019). Python Crash Course (2nd Edition). No Starch Press, Inc.
Indicative learning resources - Web based and electronic resources
Introduced but not limited to: Anaconda, Spyder, Python Crash Course, Github, Kaggle, Stackoverflow, Automatetheboringstuff, towardsdatascience and application/library specific resources
| Credit value | 30 |
|---|---|
| Module ECTS | 15 |
| Module pre-requisites | None |
| Module co-requisites | BEAM068 (For MSc Accounting and Finance only) |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 01/07/2019 |
| Last revision date | 21/09/2022 |
