1. MODULE SUMMARY
Aims and Summary
This modules hosts the Big Data Programming Project for the BSc
Computer Science course. Students work on an individual project
which requires skills and knowledge presented and developed in the
other modules studied in the semester:
5003CEM Advanced Algorithms
5005CEM Data Science
Module credits and availability
Assessment / CATS
Credits 15.0
ECTS credits 7.5
Learning credits 0.0
Open/Restricted Open
Availability on/off
campus On Campus only
Total student study
hours 150
Number of weeks 13
Faculty responsible Faculty of Engineering, Environment andComputing
Academic Year
Entry Requirements (pre-requisites and co-requisites)
Excluded Combinations
None
Pass requirements
Coursework must be at least 40% and Viva must be at least 40% and
Module Mark must be at least 40%.
Special Features
Course stages for which this module is mandatory
EECU097 MSci Stage 2 Computer Science with Artificial Intelligence
EECU096 BSc Stage 2 Computer Science with Artificial Intelligence
EECU032 BSc Stage 2 Computer Science
EECU032 None Stage 2 Computer Science
EECU034 MSci Stage 2 Computer Science
Course stages for which this module is a core option
None
2. TEACHING, LEARNING AND ASSESSMENT
Intended Module Learning Outcomes
This module helps students achieve the Course Learning Outcomes
B1, B2, B4, B5, B6, B7 and B8.
B1: COMPUTATION THINKING:
develop and understand algorithms to solve problems; measure and
optimise algorithm complexity; appreciate the limits of what may be
done algorithmically in reasonable time or at all.
B2: PROGRAMMING:
create working solutions to a variety of computational and real world
problems using multiple programming languages chosen as
appropriate for the task.
B4: DATA SCIENCE:
work with (potentially large) datasets; using appropriate storage
technology; applying statistical analysis to draw meaningful
conclusions; and using modern machine learning tools to discover
hidden patterns.
B5. SOFTWARE DEVELOPMENT: develop a product from the initial
stage of requirement / analysis all the way through development to
its final stages of testing / evaluation.
B6: PROFESSIONAL PRACTICE:
understand professional practices of the modern IT industry which
include those technical (e.g. version control / automated testing) but
also social, ethical & legal responsibilities.
B7: TRANSFERABLE SKILLS:
apply a wide variety of degree level transferable skills including time
management, team working, written and verbal presentation to both
experts and non-experts, and critical reflection on own and others
work.
B8: ADVANCED WORK:
apply the above to advanced topics selected according to the
interests of individual students.
Indicative Content
This module hosts the Stage 2 Course Project the BSc Computer
Science course. Students are presented with a task, the solution of
which is left up to them but should use the skills and knowledge
presented and developed in the other modules studied in the
semester:
5003CEM Advanced Algorithms
5005CEM Data Science
Students are free to take their project as far as they can - there is no
pre-defined end goal.
There will be lectures and labs where students can meet a supervisor
who will oversee their progress. No new theoretical material is
presented but there will be teaching and guidance on various aspects
of running a project. In the final weeks of the semester lab time will
be used to conduct individual vivas. Students will be required to
critically evaluate and reflect on their progress.
Teaching and Learning
Learning will be facilitated through a variety of methods which may
include lectures, seminars, lab, workshops, online activities and group
work. Students are expected to engage in both class and online
activities and discussions.
