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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

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


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 StudyAssessment preparation and completion10:300:30Oral Presentation
Scheduled Learning And Teaching ActivitiesLecture101:0010:00Lectures
Guided Independent StudyAssessment preparation and completion131:3031:30Coursework
Guided Independent StudyAssessment preparation and completion31:003:00Preparation for oral presentation
Guided Independent StudyAssessment preparation and completion101:0010:00Lecture follow-up
Scheduled Learning And Teaching ActivitiesPractical251:0025:00Practical sessions
Guided Independent StudyIndependent study201:0020:00Background reading & participation in discussions
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
Report1M80Extended technical project Word count: Up to 2,000 words
Oral Examination1M20Oral 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