2024/25 Taught Postgraduate Module Catalogue

OMAT5204M Data Science

15 creditsClass Size: 100

Module manager: TBC
Email: .

Taught: 1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June View Timetable

Year running 2024/25

Pre-requisite qualifications

None

Pre-requisites

OMAT5100M

Programming for Data Science

Module replaces

N/A

This module is not approved as an Elective

Module summary

Data scientists work in a wide range of fields of application. This module gives an insight into some general principles of the work of a data scientist and some of the underpinnings of artificial intelligence and statistics in the practice of data science.

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:

1. Understand the work of a data scientist
2. Understand how to acquire data and investigate the quality of data
3. Apply problem-solving skills to effectively analyse data and communicate findings for a given application scenario
4. Understand the statistical underpinnings of artificial intelligence and data science

Skills outcomes
Skills developed in this module include:

- independent investigation
- problem solving
- communication in a data science context

Syllabus

Indicative content for this module includes:

- Core skills of a data scientist: problem-solving; statistics; business acumen; communication and business understanding
- Data science scope: A day in the life of, workflows, and DS boundaries
- Data understanding and visualisation, data acquisition, data preparation and data wrangling
- Classification, similarity and clustering
- Model-fitting and evaluation
- Anomaly detection
- Association Analysis
- Big data consideration tools and techniques
- Practical applications using case studies drawn from different application domains

Teaching methods

Delivery type	Number	Length hours	Student hours
On-line Learning	5	1.00	5.00
Discussion forum	6	2.00	12.00
Seminar	1	1.50	1.50
Independent online learning hours			42.00
Private study hours			89.50
Total Contact hours			18.50
Total hours (100hr per 10 credits)			150.00

Opportunities for Formative Feedback

Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow opportunities for formative feedback from peers and tutors.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Online Assessment	MCQ and short answer questions	20.00
Assignment	Project Report	80.00
Total percentage (Assessment Coursework)		100.00

Students will resit by completing the Assignment (which covers all learning outcomes) six months after the delivery of the module.

Reading list

There is no reading list for this module

Last updated: 29/04/2024 16:18:46

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