MS4H01 - Data Analytics 04 Dec 2020 - 31 Aug 2026 | Version 1
Associated Module Information
| Module Code: | MS4H01 | ||
|---|---|---|---|
| Module Title: | Data Analytics | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Mathematical Sciences | ||
| Module Leader: | Rebecca Peters | ||
| Module Team: | Angelica Pachon, Stephanie Perkins | ||
| First Intended Intake: | JAN 2021 | Final Year of Intake: | 2025 |
| Date Closed: | |||
| Credit Value: | 10 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 101034 - statistical modelling | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 04 Dec 2020 |
| Valid To | 31 Aug 2026 |
Module Aims
To provide graduates with an understanding of the core analytical skills required for Data/AI.
To provide graduates with the practical experience in drawing insight from complex datasets such that they are able to assess practical situations and interpret real-world applications through visualisation.
Content Summary
This module provides a broad introduction to analytical methods required to understand, interpret, research, and manage complex data. It will also introduce tools and techniques for data visualisation as well as an overview of statistical inference and further multivariate techniques. The aim here will be to encourage grads to build confidence in drawing insight from data and identifying patterns and trends of interest for future exploration.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 8 |
| Seminar | 8 |
| Tutorial | 8 |
| Independent Study | 40 |
| Directed Study | 36 |
| Total Hours Selected | 100 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | To understand the concepts and application of data analysis tools and explain the wider context of their value in Data/AI. |
| LO2 | Determine and use analytical techniques to assess practical situations and interpret real-world complex data. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Set Exercise - Not Time Constrained (CW) | Set Tasks - not-time constrained 1 | Collect, analyse, and interpret a data set and present results. | 0 | 3000 | 100 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Set Tasks - not-time constrained 1 | ✔ | ✔ | |