Explore UCD

UCD Home >

ACC41250

Academic Year 2024/2025

Financial Data Technology (ACC41250)

Subject:
Accountancy
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
7.5
Module Coordinator:
Dr Daniel Peng
Trimester:
Summer
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Pass/Fail (GPA Neutral)

Curricular information is subject to change.

This module introduces accounting and finance students to the advanced applications of data science within the finance sector, focusing on how technology and statistics can be leveraged to address complex financial problems. Students will explore key statistical tools, including machine learning and econometric techniques, while gaining hands-on experience with cutting-edge technology used to acquire, manage, and analyze large financial datasets.

About this Module

Learning Outcomes:

Upon completing this module, students will:

1. Develop Analytical Competence: Demonstrate the ability to apply advanced data analytics, including machine learning and AI programming, to address complex accounting and financial reporting challenges, leveraging large datasets to enhance decision-making in accounting practices.

2. Integrate Accounting and Data Science: Understand and apply accounting principles in conjunction with data science techniques to gain insights into financial performance, audit processes, and risk assessments, thereby supporting improved reporting and compliance.

3. Utilize Emerging Technologies: Gain proficiency in emerging technologies relevant to accounting, including AI-driven audit tools, predictive analytics for financial forecasting, and risk modeling to drive innovation within accounting firms and organizations.

4. Interpret and Communicate Data Insights: Develop the ability to analyze and present accounting and financial data insights in a clear and actionable manner to stakeholders, enabling better decision-making and strategic planning at the organizational level.

5. Adapt to Industry Demands: Be equipped to meet the increasing demand for accountants who can combine accounting expertise with data analytics, playing a key role in enhancing financial transparency, regulatory compliance, and organizational efficiency through technology-driven solutions.

Indicative Module Content:

Throughout the module, emphasis is placed on real-world applications, providing a deeper understanding of the intersection between accounting and finance, technology, and data science. Topics covered will include:

1. Financial Reporting Analytics: Exploring how data science tools can enhance financial reporting accuracy, improve audit efficiency, and support the preparation of high-quality financial statements.

2. Risk Management and Internal Controls: Utilizing data analytics to assess and manage financial risks, strengthen internal controls, and detect anomalies or fraud in financial data.

3. Predictive Analytics in Accounting: Applying predictive models and machine learning to forecast financial performance, audit risks, and potential compliance issues.

4. Regulatory Compliance and Data-Driven Auditing: Examining how data science tools can be used to ensure regulatory compliance, streamline audit processes, and enhance transparency in financial reporting.

5. Emerging Technologies in Accounting: Understanding the role of AI, automation, and blockchain in transforming accounting practices, from transaction recording to auditing and financial analysis.

Student Effort Hours:
Student Effort Type Hours
Lectures

24

Specified Learning Activities

50

Autonomous Student Learning

80

Total

154


Approaches to Teaching and Learning:
This module employs a blended learning approach, combining theoretical foundations with practical, hands-on experiences to equip students with both a deep conceptual understanding and applied skills in accounting and data analytics. The teaching and learning methods include:

1. Lectures and Case Studies: Interactive lectures introduce core concepts and techniques, supported by real-world case studies from accounting and finance. These case studies allow students to explore the practical applications of data analytics in areas such as financial reporting, auditing, and risk management.

2. Project-based Assignments and Collaborative Learning: Students tackle project-based assignments that simulate real-world challenges, fostering collaboration, critical thinking, and peer learning. These projects focus on applying data science to accounting issues like fraud detection, financial forecasting, and regulatory compliance.

3. Take-home Data Analysis Exam: The final exam involves working with financial datasets using tools like Python and Excel to solve accounting-specific problems. This hands-on exam ensures that students demonstrate technical proficiency in applying data analytics to real-world tasks under time constraints.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (Take-Home): Take Home Exam on Business Case Week 10 Pass/Fail Grade Scale No
30
No
Assignment(Including Essay): Data Analytics Assignments Week 2, Week 5, Week 8 Pass/Fail Grade Scale No
60
No
Participation in Learning Activities: Class Participation Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Pass/Fail Grade Scale No
10
No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

There is no required textbook for this class. Lecture materials will be provided prior to each session. When you encounter challenges, Google and Stack Overflow are excellent resources to explore for solutions. Additionally, a DataCamp link will be sent to you before the module begins, offering essential Python tutorials to ensure you have the necessary background for the module.