Module manager: Dr Abdulrahman Altahhan
Email: a.altahhan@leeds.ac.uk
Taught: Semester 1 May to 30 June, 1 Nov to 31 Dec View Timetable
Year running 2024/25
This module is not approved as an Elective
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.
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.
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. Understanding of the statistical underpinnings of artificial intelligence and data science
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
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| On-line Learning | 6 | 1 | 6 |
| Group learning | 6 | 2 | 12 |
| Independent online learning hours | 28 | ||
| Private study hours | 104 | ||
| Total Contact hours | 18 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Private study will include directed reading and exercises and self-directed research in support of learning activities, as well as in preparation for assessments.
Independent online learning involves non-facilitated directed learning. Students will work through bespoke interactive learning resources and activities in the VLE.
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 self and peer assessment providing opportunities for formative feedback from peers and tutors.
Students will complete a formative group assessment in the same format as the final individual summative assessment, providing an opportunity for formative feedback.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assignment | Project Report | 80 |
| Assignment | Online Test | 20 |
| Total percentage (Assessment Coursework) | 100 | |
This module will be reassessed by a 100% individual assessment.
The reading list is available from the Library website
Last updated: 24/05/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team