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Big Data Programming Project
5011CEM (Academic year 2020/21)
Last uploaded: July 30, 2020
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 2020–2021
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
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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
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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.
This module also requires students to participate in additional guided
reading and self-directed study to reinforce the learning gained from
timetabled sessions. Formative assessment will be used to prepare
students for summative assessment and give students an early
indication of their progress towards the course intended learning
outcomes.
Note that the hours below may include a contribution to Activity Led
Learning projects which jointly assess multiple modules.
Note that the hours below are not those of a standard module. This is
becase this module is assessment only, taking its assessment credits
from modules 5003CEM and 5005CEM. The total number of hours for
all modules in the semester add up to the usual 600.
Assessment Components
Component Component
Type (Core
(P/F) /
Applied
Core (%))
Credits for
this
component
Learning
Outcomes
Assessed
Number of
attempts
allowed
upto final
deadline
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(Core
Components
only)
Cw Applied Core 10 B1, B2, B4-B8
Viv Applied Core 5 B4, B5, B6and B7
Method of Assessment (normally assessed as follows)
The CW is a written report (2000 word limit) on the project, focusing
on the project specification, methodology and what can be concluded
from the data, referenced appropriately.
The Viv component is an oral exam in which the student will present
their project and is questioned on their understanding of the technical
work undertaken.
Re-assessment is by making good on original CW submission with
reflection.
Date of last amendment
3. MODULE RESOURCES
Click here to access the module reading list via the
University's Resource Lists system.
Required Equipment
4. MODULE ORGANISATION
Module leader
Name Dr R Hyde
E-mail ad2105@coventry.ac.uk
Length and month of examination
None
Common Exam(s)
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None
Exam Equipment Required
None
Expected teaching timetable slots
Note that some tutorials/seminars may be provided at times other than
those shown below. Timetable information should be verified with the
School responsible for the module
No timetable information available
Subject Quality and Approval information
Board of Study Computing, Electronics and Mathematics
Subject Assessment Board Computing UG
Shortened title
Date of approval by BoS 1-Aug-2020
学霸联盟