COM7203 - Data Science and Visualisation

Objectives:

On successful completion of the module, students will be able to:

1 - Demonstrate a comprehensive understanding of data analytics, modelling and management.

2 - Apply and critically evaluate various analytical software tools for modelling and managing data.

3 - Visualise data, recognise patterns, communicate knowledge and inform decision making for organisations to create value, improve performance and create business opportunities.

4 - Gain critical understanding of data analytics’ challenges and be able to interpret the results from data and data analytics’ algorithms.

Content:

The module equips students with theoretical and practical knowledge of data analytics and data visualisation required for solving computational challenges related to the use of data and for adding business value. It provides essential theoretical and practical skills needed to understand database design, data mining, analytics and visualisation using appropriate tools.

Data Science is a broad, interdisciplinary field. Students will explore topics from a range of application areas such as sustainability, manufacturing and economy. They will work with real data to explore and solve known problems with the analysis of different types of datasets. The module takes a practical approach to learning by exploring topics through the study of applied examples.

Learning and Teaching Information:

Workshops may take different approaches relevant to the content. Students 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 academics and industry practitioners. This may then be followed up by an opportunity for students to apply techniques to given scenarios.

Workshops
Hours: 30
Intended group size: 50

Guided independent study
Hours: 270

Further details relating to assessment
Academic Paper: Students showcase their skills by reporting on the development of a suitable investigation of a real world problem, utilising a data science process. This is written in the style of a journal article.

Presentation: students present a critical evaluation of a defined topic related to data science or big data.

In this module, formative assessment is used to support the skills that contribute to the assessment. Formative assessment may include coding labs, design 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:

001 Academic Paper, 3000 word equivalent %
002 Presentation, Individual, 20 minutes %

Fact File

Module Coordinator - Lesley May
Level - 7
Credit Value - 30
Pre-Requisites - NONE
Semester(s) Offered - 7T17T2JL

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