COM7013 - Big Data Analytics and Visualisation

Objectives:

On successful completion of the module, students will be able to:
Demonstrate a comprehensive understanding of big data analytics, big data modelling and management and visualisation techniques
Apply critical evaluation to big data techniques and effectively utilise suitable software for modelling and managing big datasets
Demonstrate a comprehensive understanding of big data network analytics, text mining and social media data mining, and demonstrate the ability to apply these skills systematically
Display critical awareness of how big data analytics is utilised by managers and executives for business value creation, leading to improvements in operational, social and financial performance, and creation of new business opportunities.

Content:

The aim of this module is to equip students with theoretical and practical knowledge of big data analytics and data visualisation for business value creation. Through the use of computer software tools for modelling and managing big data, students will learn to solve complex data-centred business problems to help enterprises approach sustainability. The module is divided into two main themes: theoretical understanding and practical skills. The theoretical aspect covers the foundations of SQL and NoSQL databases design, big data analytics, text mining, social media data mining, data visualisation and database management concepts. The practical aspect focuses on building students' skills in interacting with programming techniques and software, such as Spark, for storing and analysing big data and Tableau for big data visualisation. By the end of the module, students will have the managerial insights and data-driven abilities to apply big data analytics to create business value in relation to sustainability.

Learning and Teaching Information:

Workshops may take different approaches relevant to the content. Student may be initially presented with the fundamental key concepts to enable them to understand and analyse the material. These will be interactive in nature. If possible, there will be some guest visits undertaken by academic and industry practitioners. This may then be followed up by an opportunity for students to apply techniques to given scenarios.

Workshops
Hours: 40
Intended Group Size: 50

Guided independent study
Hours:260

Technical Report: A technical report will be generated according to working on an analytical real-world big data project. Students will select or conceive of a scenario around which they will design their analysis. They will investigate how big data analytics is utilised by managers and executives for business value creation. They will show an understanding of the requirements concerning the modelling, management, visualisation and handling of big data. The students will be required to deliver a set of tasks as follows:

- big data analytics and queries using software such as Spark SQL or PySpark
- visualisation of the queries outcomes and
- project documentation, workability and interpretation of outcomes.

Presentation: Students will be required to present their analysis along with a critical evaluation of the proposed solution and decisions around the modelling, management, visualisation and handling of data.

In this module, formative assessment will be used to support the skills that contribute to the assessment. Formative assessment may include coding labs, visualisation and modelling tasks, case study presentations, short quizzes or specific research tasks. Formative feedback will be an ongoing process within class sessions.

Students should refer to the Module Handbook for further details on the module learning, teaching and assessment strategies.

Assessment:

Fact File

Module Coordinator - PRS_CODE=
Level - 7
Credit Value - 30
Pre-Requisites - NONE
Semester(s) Offered - 7T2

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