CSC8631 : Data Management and Exploratory Data Analysis
- Offered for Year: 2024/25
- Module Leader(s): Dr Joe Matthews
- Owning School: Computing
- 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 understanding of 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 for Data Science
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 | 10 | 1:00 | 10:00 | Lecture follow-up |
| Guided Independent Study | Assessment preparation and completion | 1 | 0:30 | 0:30 | Oral Presentation |
| Scheduled Learning And Teaching Activities | Lecture | 8 | 1:00 | 8:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 31:30 | 31:30 | Coursework |
| Scheduled Learning And Teaching Activities | Practical | 8 | 2:00 | 16:00 | Practical sessions |
| Guided Independent Study | Independent study | 31 | 1:00 | 31:00 | Background reading & participation in discussions |
| 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 |
|---|---|---|---|---|
| Report | 1 | M | 80 | Extended technical project Word count: Up to 2,000 words |
| Oral Examination | 1 | M | 20 | Oral presentation- presentation of the methods and results from the coursework - length 15 minutes |
Assessment Rationale And Relationship
This module develops learners' skills in undertaking data science 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/
- CSC8631's Timetable