Module ACCT3011: Big Data Analytics

Department: Accounting

ACCT3011: Big Data 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 big data analytics. In particular, on the successful completion of this module students will be able to:
• explain how big data analytics has been successfully used in various industries; including business, finance, accounting and auditing;
• understand the theoretical foundation of machine learning;
• effectively use machine learning algorithms to develop big data analytics tools in support of decision making;
• apply appropriate machine learning techniques to analyse big data set;
• utilise statistical packages and software tools (e.g. MATLAB / Octave / Python) to develop big data and machine learning models;
• design and analyse machine learning experiments.
• critically discuss data privacy and data security issues that emerge when developing big data models;
• apply appropriate machine learning techniques to analyse big data sets;
• utilise statistical packages/ software tools (e.g. MATLAB/Octave/Python) to develop big data models.

Content

• The fundamentals of big data.
• Machine learning and Artificial Intelligence (AI) in business, finance, accounting and auditing practices.
• Linear regression and Multivariant Methods.
• Parametric & Non-Parametric Methods.
• Deep Learning.
• Introduction to Artificial Neural Networks (ANNs).
• Clustering and K-Means.
• Machine learning and artificial Intelligence (AI) in business, finance, accounting and auditing practices.

Learning Outcomes

• By the end of the module students should be able to:
• demonstrate advanced knowledge and understanding of: the theory of data, different types of data analytics tools and techniques, the role of machine learning and artificial intelligence in developing data models for business, finance, accounting and auditing practices, along with ethical issues that working with data can present;
• understand the application of supervised and unsupervised machine learning;
• develop clear understanding of different data behaviour and the appropriate analytics modelling techniques;
• demonstrate advanced knowledge and understanding of the implementation of AI techniques to resolve accounting and audit problems and challenges.

• By the end of the module students should be able to:
• demonstrate different the evolution of machine learning and AI;
• identify different applications of machine learning and AI in the accounting and audit profession;
• competent in data coding, data evaluation and data applications in business, finance, accounting and auditing.
• competent in manipulating data and programming;
• capable of implementing different machine learning and AI tools to develop a systematic modelling network.

• Computer literacy
• Data coding and programming 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 lectures and practical workshops that will demonstrate the development of various data models; students may use different software tools such as MATLAB/Octave, and/or Python.
• Formative assessment will consist of programming quizzes.
• Summative assessment will consist of one individual project. The project will be based on coding techniques covered in the workshops.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Individual programming quizzes

■ Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.