Module Search

COMP2271: DATA SCIENCE

Please ensure you check the module availability box for each module outline, as not all modules will run in each academic year. Each module description relates to the year indicated in the module availability box, and this may change from year to year, due to, for example: changing staff expertise, disciplinary developments, the requirements of external bodies and partners, and student feedback. Current modules are subject to change in light of the ongoing disruption caused by Covid-19.

Type	Open
Level	2
Credits	20
Availability	Available in 2023/24
Module Cap	None.
Location	Durham
Department	Computer Science

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 Combinations of Modules

None

Aims

To introduce techniques for capturing, cleaning and analysing data
To explore how different types of information can be represented and processed

Content

Data capture and analytics
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, validated and analysed;
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 visualisation and analysis;
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
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

More information

If you have a question about Durham's modular degree programmes, please visit our FAQ webpages, Help page or our glossary of terms. If you have a question about modular programmes that is not covered by the FAQ, or a query about the on-line Undergraduate Module Handbook, please contact us.

Prospective Students: If you have a query about a specific module or degree programme, please Ask Us.

Current Students: Please contact your department.