COM6103 - Data Science

Objectives:

Content:

Data Science is a broad, interdisciplinary field. Students will explore topics from a range of application areas and 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. Students are educated in the principles of data science and taught the analytical thinking approaches needed to extract business value from data. The module aims to prepare students for roles which combine data analysis with data-driven decision making. It recognises the role of big data analytics in business by introducing students to the basics of big data processing. Previous knowledge informs a student’s approach and strengthens their business insights. Topics included may address large-scale data management, storage and retrieval founded in mathematics and statistics

Learning and Teaching Information:

The learning and teaching method for this module is centred on a series of predominantly practical guided activities, initiated by short theoretical introductions. Applying data science skills to real-world problems helps students understand the impact and relevance of their work. Practical examples illustrate the potential of data science to drive innovation and improve decision-making.

Case studies and projects could be used to explore applications such as customer segmentation, predictive modelling and sentiment analysis, demonstrating how data science can be used in various domains. The goal is to develop the students’ critical perspective while gaining up-to-date domain knowledge through practical work. Each session includes preliminary exercises and readings based on online materials conducted by students on their own. After the workshop, students individually consolidate their understanding through additional activities and further readings.

Workshops
Hours 40
Group Size All Cohort

Guided independent study
Hours: 260

Data Science Presentation: Students deliver an analytical data report as a presentation with on-screen visuals. They select or conceive a scenario around which they design their analysis. Students investigate the legal, ethical, equality and diversity requirements concerning the collection, storage and handling of data.

Data Science Portfolio: Students build up a data science portfolio showcasing their skills. The portfolio takes the form of a website and/or Git repository. Projects in the portfolio may centre on scenarios introduced in the tutorials or those defined by the student. Where a student-defined project relies on the collection or handling of sensitive data, ethical approval must first be obtained through the Faculty's Research Ethics and Integrity Committee.

In computer science classes, formative assessment serves to bolster the skills essential for module success. This includes engaging in practical labs, undertaking design and modelling tasks, delivering case study presentations, completing short quizzes and conducting specific investigation tasks. The provision of formative feedback is integrated seamlessly into class sessions, ensuring an ongoing and iterative process to enhance learning outcomes.

Full details are available in the Module Handbook.

Assessment:

001 Data science presentation; 1600 word equiv; end of semester 1 40%
002 Data science portfolio; 2400 word equiv; end of semester 2 60%

Fact File

Module Coordinator - Antesar Shabut
Level - 6
Credit Value - 30
Pre-Requisites - NONE
Semester(s) Offered - 6YL

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