NG3S505 - Data Literacy 01 Jul 2021 - 31 Aug 2027 | Version 1
Associated Module Information
| Module Code: | NG3S505 | ||
|---|---|---|---|
| Module Title: | Data Literacy | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Engineering | ||
| Faculty Sub Group: | Aeronautical Engineering | ||
| Module Leader: | David Scammell, Rae Gordon | ||
| Module Team: | Alexandre Oleon, Sivagunalan Sivanathan, Gary Dornan, Adrian Pitman | ||
| First Intended Intake: | SEP 2026 | Final Year of Intake: | 2026 |
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 6 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100755 - data management | 101027 - numerical analysis | |
| HECOS Code Weighting: | 40 | 60 | |
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 01 Jul 2021 |
| Valid To | 31 Aug 2027 |
Module Aims
This module will provide students with an understanding of the importance of data analytics and how it is applied to real world applications.
Content Summary
Overview of Data Literacy
- Why Analytics
- A culture of Data Literacy
- Data Literacy Adoption
- Data Storytelling
Data Fundamentals
- Understanding Data
- Understanding Aggregations
- Understanding Distributions
Foundational Analytics
- Understanding Signal and Noise
- Correlation and Causation
- Confidence Intervals
- Analytical A/B Testing
- Simple Linear Regression
- Hypothesis Testing
- Design of Experiments
Data-Informed Decision Making
- Introduction to Data-Informed Decision Making
- Data-Informed Decision-Making Framework
- Decision Making Analytic Techniques
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 24 |
| Practical classes and workshops | 48 |
| Independent Study | 72 |
| Directed Study | 36 |
| Problem / challenge based learning | 20 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | To be able to assess, analyse and draw conclusions with Data. |
| LO2 | Use analytical tools to evaluate data sets and use results to make informed decisions. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Portfolio 1 | Use analytical tools to perform analysis of stored data that would help with predictive maintenance, and manufacturing performance and efficiency / Report detailing the observations | 0 | 3000 | 100 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Portfolio 1 | ✔ | ✔ | |