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Durham University

Faculty Handbook 2022-2023

Module Description

Please ensure you check the module availability box for each module outline, as not all modules will run each academic year.

Department: Computer Science

COMP2271: DATA SCIENCE

Type Open Level 2 Credits 20 Availability Available in 2022/23 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



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