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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.

We are witnessing the shifting landscape of further finance and accounting digitalisation. Change can be seen on both the micro and macro level. We experience cycles of technological innovation, which lead to organisational change and transformation. Once the value of data is understood, data can be turned into information and powerful insight.

About this Module

Learning Outcomes:

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

- Assess the relationship between technology and a firm’s strategy.
- Advise on data and technology strategy.
- Understand Data’s relationship to strategic assets and transformation.
- Demonstrate understanding of the Data Analytics ecosystem (including emerging technologies).
- Assess the value and maturity of Data.
- Describe the relation between data and information.
- Understand and define the principles of Data Analytics.
- Display a developed awareness of potential issues within Data Analysis and the makeup of good-quality data.
- Produce a Step-by-Step Plan for performing Data Analysis.
- Demonstrate understanding of the Analytics Lifecycle and the CRISP-DM methodology.
- Describe and discuss Data Protection issues and Ethics relevant to Data Analytics.
- Format data to create insights and inform strategic decisions.

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

12
Autonomous Student Learning

78
Lectures

8
Computer Aided Lab

16
Total

114

Approaches to Teaching and Learning:
This module will give students theoretical insights into current trends in accounting analytics and provide a practical overview of accounting analytic tools, including Microsoft, Alteryz and Tableau.

Students will also engage in online certificates of these tools and undertake group projects which will enhance their data analytical skills.

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, and completing a specified learning course, accounting for 50% of the module grade. Week 10 Alternative non-linear conversion grade scale 50% No

50

 No
Group Work Assignment: A group assignment involving the selection of a dataset, visualising the data and constructing a dashboard, producing a report on the analysis and findings, and presenting the 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
• Group/class feedback, post-assessment
• Peer review activities

How will my Feedback be Delivered?

Upon grading, students will receive their individual marks.

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