Module manager: TBC
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Taught: Semester 1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June View Timetable
Year running 2024/25
None
| OMAT5100M | Programming for Data Science |
N/A
This module is not approved as an Elective
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.
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. Understand the statistical underpinnings of artificial intelligence and data science
Skills developed in this module include:
- independent investigation
- problem solving
- communication in a data science context
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 | 5 | 1 | 5 |
| Discussion forum | 6 | 2 | 12 |
| Seminar | 1 | 1.5 | 1.5 |
| Independent online learning hours | 42 | ||
| Private study hours | 89.5 | ||
| Total Contact hours | 18.5 | ||
| Total hours (100hr per 10 credits) | 150 | ||
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.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Online Assessment | MCQ and short answer questions | 20 |
| Assignment | Project Report | 80 |
| Total percentage (Assessment Coursework) | 100 | |
Students will resit by completing the Assignment (which covers all learning outcomes) six months after the delivery of the module.
There is no reading list for this module
Last updated: 4/29/2024
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