Module ACCT2151: Accounting Information Systems & Analytics

Department: Accounting

ACCT2151: Accounting Information Systems & Analytics

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• This module will aim to introduce students to the concepts, terminologies, tools and technologies of Accounting Information Systems (AIS) and data analytics using large datasets. In particular, on the successful completion of this module students will be able to:
• understand the key features of AIS, transaction and data processing systems;
• examine the value and data generated by the Enterprise Resource Planning (ERP) systems;
• critically discussing the key accounting information cycles, such as revenue cycle, expenditure cycle, production cycle and payroll cycle
• identify the tools and technologies required for data analytics;
• critically discuss the value of big data analytics to business, finance, accounting and auditing;
• introduce data visualisation and visual analytics;
• examine advanced graphical presentations in auditing and accounting using Tableau;
• critically interpret descriptive, diagnostic, predictive and prescriptive data analytics findings generated using Stata.

Content

• The fundamentals of AIS.
• The key ERP systems and accounting cycles.
• Descriptive data analytics techniques using Stata.
• Data visualisation using Tableau.
• Diagnostic data analytics using Stata.
• Predictive data analytics using linear regressions using Stata.
• Prescriptive data analytics.

Learning Outcomes

• Demonstrate the key features of an accounting information system.
• Demonstrate different structured and unstructured data, considering its volume, variety, veracity and velocity.
• Identify different types of data required to solve various problems and challenges using scientific assumptions and hypotheses to deal with them, from a critical perspectives.
• Critically evaluate the different business, accounting and auditing practices and the role of technologies and data analytics towards enhancing and improving these practices.
• Demonstrate clear understanding of data visualisation techniques.
• Demonstrate advanced knowledge and understanding of: the theory of data, different types of data analytics tools and techniques.

• Be competent in data collection, data mining, data management, data classification, data evaluation and data applications in business, finance, accounting and auditing using Stata.
• Be able to evaluate different challenges for data analytics in business, finance, accounting and auditing, such as: data ethics, confidentiality, cybersecurity, data security and fraud.
• Be able to visualise different data sets and extracting underlying trends and patterns.

• Computer literacy.
• Data analytics and visualisation skills.
• The ability to communicate effectively: communicating complex ideas orally and in writing.
• The ability to think critically and creatively and to argue coherently.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• The module will be delivered in a series of workshops and will demonstrate the development of various data models; students may use different software tools such as Tableau and Stata.
• Formative assessment will consist of analytics assignments.
• Summative assessment will consist of one individual project. The project will be based on analytics techniques covered in the workshops.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Individual analytics assignments

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University