CEG1713 : Data Science (Inactive)
- Inactive for Year: 2024/25
- Module Leader(s): Professor Philip James
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
Semester 2 Credit Value: | 10 |
ECTS Credits: | 5.0 |
European Credit Transfer System |
Aims
This module will provide students with the foundations to manipulate digital data and carry out computations using programs and scripts. It provides an introduction to fundamental programming principles including data processing and input and output. It utilises current scripting tools, languages and packages that are essential to the spatial scientist including Python.
Outline Of Syllabus
This module covers:
• Python syntax and tools
• Data types and calculations
• Conditional statements and branching
• Loops and repetition
• Data in lists
• File input and output
• Libraries and scripts
• Data visualisation
Teaching Methods
Teaching Activities
Category | Activity | Number | Length | Student Hours | Comment |
---|---|---|---|---|---|
Guided Independent Study | Assessment preparation and completion | 2 | 10:00 | 20:00 | 2 reports and associated scripts. |
Scheduled Learning And Teaching Activities | Lecture | 20 | 1:00 | 20:00 | Practice based teaching - so requires access to computers for lectures ideally set up as an independent classroom (ie not suitable for large clusters). |
Scheduled Learning And Teaching Activities | Practical | 4 | 2:00 | 8:00 | PC practical / support session |
Guided Independent Study | Independent study | 10 | 1:00 | 10:00 | Practice of material delivered through lectures. |
Guided Independent Study | Independent study | 1 | 42:00 | 42:00 | Background reading and practice |
Total | 100:00 |
Teaching Rationale And Relationship
• Students will be presented with new information and concepts through interactive lectures. Using interactive tools the students will be able to practice key concepts during the lectures
• Practicals will directly address key programming constructs and problems and provide practice in data handling and analysis
• Lectures and practicals will be used to demonstrate the Python scripting language and the Jupyter notebook environment for data science
• Practicals will demonstrate the use of external libraries.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
Description | Semester | When Set | Percentage | Comment |
---|---|---|---|---|
Report | 2 | M | 50 | Script output, testing and reflective analysis (Whole Circle Bearings) |
Report | 2 | A | 50 | Script output, testing and reflective analysis (Centraility measures) |
Assessment Rationale And Relationship
• The coursework elements will assess the students’ ability to solve problems through the creation of scripts utilising data processing.
• The coursework elements will require the use of external libraries to solve some of the problems set
• The coursework elements will require the use of visualisation techniques for data
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CEG1713's Timetable