MAS8503 : Data Management and Exploratory Data Analysis
- Offered for Year: 2024/25
- Module Leader(s): Dr Daniel Henderson
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Aims
This module explores the principles of data management and exploratory data analysis. Furthermore, we introduce the underlying technologies and computational tools to support automation and reproducibility in data analysis.
Specifically, the module aims to equip the students with the following knowledge and skills:
• To understand the principles of the scientific method and how it is applied in computational analyses
• To understand methods of data characterisation and data processing
• To understand the principles of knowledge representation and constructing data models
• To understand the technologies that support analysis pipelines
• To understand end-to-end system design.
Outline Of Syllabus
1. Scientific method in computational analyses
2. The software lifecycle
3. The data lifecycle
4. Variable characterisation and experimental design
5. Exploratory data analysis
6. Open Science and Reproducibility
7. Data Architectures
8. System design, microservices and workflows
9. Developing data products
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 3 | 1:00 | 3:00 | Preparation for oral presentation |
| Guided Independent Study | Assessment preparation and completion | 1 | 0:30 | 0:30 | Oral Examination |
| Guided Independent Study | Assessment preparation and completion | 20 | 1:00 | 20:00 | Background reading & participation in discussions |
| Guided Independent Study | Assessment preparation and completion | 5 | 0:30 | 2:30 | Preparation for oral examination |
| Guided Independent Study | Assessment preparation and completion | 10 | 1:00 | 10:00 | Lecture follow-up |
| Guided Independent Study | Assessment preparation and completion | 1 | 2:00 | 2:00 | Non-synchronous online presentations session with participation in non-synchronous peer Q&A |
| Scheduled Learning And Teaching Activities | Lecture | 10 | 1:00 | 10:00 | Lectures (Present in person) |
| Guided Independent Study | Assessment preparation and completion | 27 | 1:00 | 27:00 | Coursework |
| Scheduled Learning And Teaching Activities | Practical | 25 | 1:00 | 25:00 | Practical sessions (Present in person) |
| Total | 100:00 |
Teaching Rationale And Relationship
Lectures explain the underpinning principles for the module and technologies that support data management and exploratory data analysis. Lectures are complemented by supervised practical sessions to guide the application of these principles using suitable computational tools. The practical work builds up experience working with a computational toolset that is used to complete a substantive project working with data from a real-world context.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Oral Examination | 1 | M | 20 | Oral presentation- presentation of the methods and results from the coursework - length 15 minutes |
| Report | 1 | M | 80 | Extended technical project Word count: Up to 2,000 words |
Zero Weighted Pass/Fail Assessments
| Description | When Set | Comment |
|---|---|---|
| Oral Examination | M | A structured interview/discussion including a software demonstration & reflection on the key learning objectives of coursework. |
Assessment Rationale And Relationship
This module develops learners' skills in undertaking business analytics projects and communicating their findings in both written and verbal forms; therefore we require two summative assessments to capture both modalities of communication.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MAS8503's Timetable