Unit Code: LEO5310

Description:

Contemporary analytics requires skills in data identification, exploration, and presentation. This unit develops basic skills in the use of SQL and R to clean and explore data. It then focuses on the use of R, as a programming language, for further manipulation and visualisation of complex data. Specifically, it promotes the use of R for development of dashboards, presentation slides and reports. It also provides instruction on the use of Generative AI to draft R code. Through the strategic application of these tools, commonly used throughout the data analytics cycle, the unit emphasises principles of information design in the exploration and visualisation of complex data, thereby improving the usability of analytical findings to support decision-making and address dynamic challenges. Examples will included as a foundation for students to develop their own application of data exploration and visualisation.

Unit Code: LEO5311

Description:

Quantitative data analysis lies at the centre of the data analytics cycle – the critical stage when data is transformed into findings and insights. This unit introduces the use of Microsoft Excel for statistical analysis and basic modelling methods. While covering a broad range of quantitative methods, it provides specific instruction on the use of confidence intervals, linear regression, and causality analysis to generate findings that can inform decisions across a wide range of applications. Examples from marketing, economics, accounting and related disciplines are included as a foundation for students to design and execute their own application of quantitative data analysis.

Unit Code: LEO5312

Contact Hours: 20-24 hours of study per week

Description:

Data reporting and presentation plays a vital role in the data analytics cycle. This unit develops advanced skills in the use of Power BI for the exploration, analysis, and presentation of data for decision-making. Specifically, it focuses on transformation of raw data into a worked form for initial visualisation and descriptive analysis. It then moves through the data analytics cycle to provide instruction for intensive use of Power BI to generate reports and dashboards in alignment with stakeholder needs. Examples of analysis, visualisation and reporting using Power BI will be included as a foundation for students to design and execute their own application of reporting and presentation.

Unit Code: LEO5104

Contact Hours: 20-24 hours of study per week

Description:

The modern organisation generates a significant amount of data that is used to enhance decision making and improve ESG (environmental, social, governance) performance. Implementing analytics across an enterprise should establish a data-driven environment using the right tools and platforms to convert data into meaningful information for the right end users.

This unit explores how contemporary business intelligence, data analytics and reporting capabilities (e.g., Excel, Power BI, Tableau) can be used effectively to communicate actionable insights to stakeholders. You will consider the strategic and technical considerations of designing, delivering and utilising a successful enterprise analytics solution for today’s global challenges.

Learning Objectives

• Demonstrate an understanding of the strategies and challenges of implementing an enterprise analytics solution
• Recognise the types of data analytics in an organisation and how these can inform decision making and address business problems
• Develop and understand data storytelling skills combined with analytical resources to foster a data-driven enterprise
• Identify and critically evaluate the frameworks, tools and applications that can be used in an enterprise setting to deliver actionable reporting and insights.

Unit Code: ETO5513

Contact Hours: 20-24 hours of study per week

Description:

‘Collaborative and reproducible practices’ aims to enhance your teamwork skills to create reproducible data analyses. You will learn how to work effectively with a group to construct a collaborative data story. These skills are practised in a team setting data analysis group project.

Unit Code: ITO4133

Contact Hours: 20-24 hours of study per week

Description:

‘Introduction to Python’ teaches programming fundamentals and the Python language. You will develop a foundational understanding of program design and implementation of algorithms. This unit will cover basic input and output, program control structures, basic data structures and problem solving strategies.

Unit Code: LEO5314

Contact Hours: 20-24 hours of study per week

Description:

Machine learning enables organisations to unlock insights and make informed predictions by leveraging predictive, diagnostic and prescriptive analysis. This unit covers the fundamentals of machine learning algorithms to analyse complex data with a specific focus on the use of R programming. The unit equips students with practical skills in supervised and unsupervised learning, including regression models, kNN, decision trees, random forests and clustering analysis. Examples of contemporary approaches to machine learning will be included through case across business disciplines.

Unit Code: ACO5170

Contact Hours: 20-24 hours of study per week

Description:

‘Predictive analytics in business’ introduces you to Big Data and predictive analytics using financial information. You will learn hands-on skills to manage large-scale financial databases and build predictive models that support strategic and investment decision making. The unit will introduce you to SQL coding and develop your skills in SAS statistics software.