2022/23 Taught Postgraduate Module Catalogue
COMP5122M Data Science
15 creditsClass Size: 315
Module manager: Professor Roy Ruddle
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2022/23
This module is not approved as an Elective
ObjectivesThe aim of the module is for students to understand methods of analysis that allow people to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.
1. Understand the work of a data scientist
2. Understand issues relating to data governance
3. Understand how to acquire, link and investigate the quality of data
4. Apply problem-solving skills to effectively analyse data and communicate findings for a given application scenario
5. Understand how analysis workflows may be scaled up to meet the challenges posed by Big Data
Overview: Work context and core skills of a data scientist (problem-solving; statistics; business acumen; communication). Data governance: ethics, privacy, regulations, policies, and provenance. Analysis lifecycle: problem understanding, data acquisition (data types; record linkage; Open Data), data quality (completeness, correctness, concordance, currency & plausibility), analysis techniques, and communicating the results. Practical application using case studies drawn from different application domains such as R and Tableau. Scale-up of analysis for Big Data.
|Delivery type||Number||Length hours||Student hours|
|Private study hours||130.00|
|Total Contact hours||20.00|
|Total hours (100hr per 10 credits)||150.00|
Private studySome of this centres on the practical work that underpins the module and the coursework. Students will also be provided with a recommended reading list including books, papers and online resources. They will receive guidance on where to focus this reading and advice on how it links to module content.
Opportunities for Formative FeedbackContribution to seminars (typically weekly). Progress in practicals (typically weekly). Engagement and standard of coursework.
Methods of assessment
|Assessment type||Notes||% of formal assessment|
|Total percentage (Assessment Coursework)||60.00|
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
|Exam type||Exam duration||% of formal assessment|
|Online Time-Limited assessment||2 hr||40.00|
|Total percentage (Assessment Exams)||40.00|
This module will be reassessed by an online time-limited assessment.
Reading listThe reading list is available from the Library website
Last updated: 01/06/2022 16:59:02
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