Module ACCT3011: Big Data Analytics

Department: Accounting

ACCT3011: Big Data Analytics

Tied To

• N204: Accounting and Management with Placement Year
• N206: Accounting and Management with Study Abroad
• N302: Accounting and Finance with Placement Year
• N304: Accounting and Finance with Study Abroad
• N408: Accounting
• N409: Accounting with Placement Year
• NN42: Accounting and Management
• NN43: Accounting and Finance

Pre-requisites

Corequisites

Excluded Combinations

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:
• examine the evolution of big data analytics including ethical issues that have arisen;
• examine the value created by big data analytics in the corporate world and its applicability in business, finance, accounting and auditing;
• define and interpret the core concepts and terminologies of big data analytics;
• identify the tools and technologies required for big data analytics;
• critically discuss the value of big data analytics to business, finance, accounting and auditing;
• explain how big data analytics has been successfully used in various industries; including business, finance, accounting and auditing;
• effectively use machine learning algorithms to develop big data analytic tools in support of decision making;
• critically discuss data privacy and data security issues that emerge when developing big data models;
• critically discuss data discrimination and potential bias in data;
• apply appropriate machine learning techniques to analyse big data sets;
• utilise statistical packages/softare tools (e.g. Tableau, STATA, MATLAB/Octave/Python) to develop big data models.

Content

• The fundamentals of big data.
• Storage and analysing big data.
• Data ethics, data privacy and data security.
• Big data analytics techniques (descriptive, diagnostic, prescriptive and predictive analytics).
• Audit data analytics (continuous auditing and eco systems).
• 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 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 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.

• By the end of the module students should be competent in data collection, data mining, data management, data coding, data classification data evaluation and data applications in business, finance, accounting and auditing.
• Students will 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.
• The ability to develop various machine learning models for big data analytics.

• 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
• The ability to develop various machine learning models for big data analytics.

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 in a TEAL space and will demonstrate the development of various data models; students may use different software tools such as Tableau, STATA, MATLAB/Octave, and/or Python.
• Formative assessment will consist of programming quizzes.
• Summative assessment will consist of two individual projects. The first project will be based on data analytics tools, the second project will be based on coding techniques covered in the workshops.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment

Individual programming quizzes