COM7015 - Fundamentals of Data Science

Objectives:

On successful completion of the module, students will be able to:
Demonstrate an understanding of fundamental algorithms and data structures used to solve data challenges
Apply and evaluate various analytical software tools
Visualise data in multiple ways
Recognise patterns from analysing data, communicate knowledge and inform decisions.

Content:

The aim of this module is to help students develop an understanding of fundamental algorithms and data structures required for solving computational challenges related to the use of data. It will provide essential exploratory techniques to describe data and to introduce computational methods for solving problems in application areas relevant to economic, environmental and societal issues surrounding sustainability practices.

Data Science is a broad, interdisciplinary field. Students will explore topics from a range of application areas in relation to sustainability, such as sustainable 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: 120

E-Portfolio: across the module, students will build up a data science portfolio showcasing their skills. The portfolio will take the form of a website and/or git repository. Projects in the portfolio may centre on real-world 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 & Integrity Committee.

In this module, formative assessment will be 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 E-Portfolio; 3,000 word equivalent; end of term 1 100%

Fact File

Module Coordinator - PRS_CODE=
Level - 7
Credit Value - 15
Pre-Requisites - NONE
Semester(s) Offered - 7T1

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