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ACC41220

Academic Year 2024/2025

Accounting Analytics (ACC41220)

Subject:
Accountancy
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Karolis Matikonis
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The accounting field is experiencing a profound transformation, fueled by increasing digitalisation and the central importance of data. As organisations come to understand the growing value of data, the ability to transform raw data into meaningful information and actionable insights has become essential. This module is designed to equip students with the knowledge and tools to effectively manage and analyse data, empowering them to make informed strategic decisions. By bridging the gap between data analysis and strategic application, this module prepares students to navigate and lead in the rapidly evolving digital landscape of accounting.

About this Module

Learning Outcomes:

By the end of this module, students will be able to:

-Critically evaluate and manage diverse data sources to ensure informed and robust analysis, demonstrating the ability to select, organise, and justify the use of data in accounting analytics.
-Apply visual and analytical techniques to interpret financial data and effectively communicate actionable insights to various stakeholders.
-Advise on the integration of technology and data strategies that enhance decision-making and organisational performance.
-Demonstrate a thorough understanding of ethical considerations in accounting analytics.

Indicative Module Content:

This module introduces the fundamental concepts and applications of data analytics in accounting. It begins by exploring the relevance of data analytics within the accounting profession, providing a foundation for understanding how data can be used to inform decisions.

Students will gain an overview of key aspects of data, including its types, sources, and the basics of data management. The module will cover essential techniques for collecting, organising, and maintaining data to support accounting practices.

Basic statistical concepts will be reviewed to prepare students for practical exposure to data visualisation. Using commonly available tools like Excel, Power BI, and Tableau, students will learn how to create visual representations of financial data that can aid in decision-making.

As the module progresses, students will be introduced to more advanced analytics techniques, with opportunities to apply these methods in practical scenarios. Additionally, the module will touch upon the integration of technology and data strategies within accounting, supported by selected theories and case studies.

The module will also include discussions on ethical considerations in accounting analytics, emphasising the importance of integrity and compliance in data-driven environments.

This module aims to provide students with a solid introduction to accounting analytics, equipping them with basic skills and knowledge that can be further developed and applied in professional contexts.

Student Effort Hours:
Student Effort Type Hours
Lectures

8

Computer Aided Lab

16

Specified Learning Activities

12

Autonomous Student Learning

78

Total

114


Approaches to Teaching and Learning:
The Accounting Analytics module combines various teaching methods to ensure students gain both theoretical knowledge and practical skills. Key approaches include:

-Lectures: Core concepts are introduced through lectures, laying the foundation for further learning.
-Practical Exercises: Students engage in hands-on tasks using their own laptops with required software downloaded in advance.
-Group Work: Collaborative projects encourage teamwork and peer learning.
-Reflective Learning: Assignments prompt students to reflect on their understanding and application of the material.
-External Training: Additional training and exercises ensure continuous skill development.

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
Individual Project: Individual assignment involving tasks with data visualisation software, reporting on the analysis and findings. Week 10 Alternative non-linear conversion grade scale 50% No
50
No
Group Work Assignment: A group assignment involving the selection of a dataset, analysing the data and communicating insights. Week 12 Alternative non-linear conversion grade scale 50% No
50
No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Upon grading, students will receive their individual marks.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 5, 7, 10 Mon 09:00 - 10:50
Autumn Lecture Offering 1 Week(s) - 9 Mon 14:30 - 16:20
Autumn Lecture Offering 1 Week(s) - 4, 5, 6, 7, 9, 10 Tues 09:00 - 10:50
Autumn Presentation Offering 1 Week(s) - 12 Wed 09:00 - 13:50