Undergraduate Programme and Module Handbook 2020-2021 (archived)
Module COMP2271: DATA SCIENCE
Department: Computer Science
COMP2271: DATA SCIENCE
| Type | Open | Level | 2 | Credits | 20 | Availability | Available in 2020/21 | Module Cap | None. | Location | Durham |
|---|
Prerequisites
- COMP1051 Computational Thinking AND (COMP1021 Maths for Computer Science OR MATH1551 Maths for Engineers and Scientists OR (MATH1561 Single Mathematics A AND MATH1571 Single Mathematics B) OR (MATH1061 Calculus I AND MATH1017 Linear Algebra I))
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To introduce techniques for capturing, analysing, cleaning and analysing data
- To explore how different types of information can be represented and processed
Content
- Data capture and cleaning
- Probability and statistics
- Graphics and visualisation
- Image processing
Learning Outcomes
Subject-specific Knowledge:
- On completion of the module, students will be able to demonstrate:
- an understanding of how data are captured and validated
- an understanding of fundamental principles of probability and how they are used in statistics
- an understanding of how images are represented, and how they can be processed and generated
Subject-specific Skills:
- On completion of the module, students will be able to demonstrate:
- an ability to collect and combine data from multiple sources
- an ability to select and apply appropriate statistical measures to data sets
- an ability to generate appropriate data visualisations
- an ability to select and apply appropriate image processing techniques
Key Skills:
- On completion of the module, students will be able to demonstrate:
- an ability to undertake reasoning in different application areas
- an ability to communicate technical information
- an ability to use general IT tools
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures enable the students to learn new material relevant to data science.
- Practical classes enable the students to put into practice learning from lectures and strengthen their understanding through application.
- Formative and summative assessments assess the application of methods and techniques, and examinations in addition assess an understanding of core concepts.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| lectures | 44 | 2 per week | 1 hour | 44 | |
| practical classes | 21 | 1 per week | 2 hours | 42 | |
| preparation and reading | 114 | ||||
| total | 200 |
Summative Assessment
| Component: Examination | Component Weighting: 50% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Examination | 2 hours | 100% | Yes |
| Component: Coursework | Component Weighting: 50% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Summative Assignment | 100% | Yes | |
Formative Assessment:
Formative exercises are given during practical sessions
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University