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2019/20 Taught Postgraduate Module Catalogue

COMP5122M Data Science

15 creditsClass Size: 160

Module manager: Roy Ruddle
Email: r.a.ruddle@leeds.ac.uk

Taught: Semester 1 View Timetable

Year running 2019/20

This module is not approved as an Elective

Objectives

The 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.

Learning outcomes
On completion of this module, students should be able to:
- Understand the work of a data scientist
- Understand issues relating to data governance
- Understand how to acquire, link and investigate the quality of data
- Apply problem-solving skills to effectively analyse data and communicate findings for a given application scenario
- Understand how analysis workflows may be scaled up to meet the challenges posed by Big Data


Syllabus

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 (http://www.tableausoftware.com/data-visualization-software; provided through the Tableau for teaching programme). Scale-up of analysis for Big Data.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lecture101.0010.00
Practical101.0010.00
Seminar101.0010.00
Private study hours120.00
Total Contact hours30.00
Total hours (100hr per 10 credits)150.00

Private study

Some 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 Feedback

Contribution to seminars (typically weekly). Progress in practicals (typically weekly). Engagement and standard of coursework.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
AssignmentAssignment50.00
Total percentage (Assessment Coursework)50.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated


Exams
Exam typeExam duration% of formal assessment
Open Book exam2 hr 00 mins50.00
Total percentage (Assessment Exams)50.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 30/04/2019

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