New Module Form
Essential Information Required for
Module Manager
ACADEMIC YEAR ___________
Module Detail
Title Web Science and Analytics
(maximum 50 characters)
Description
Web Science is concerned with techniques for understanding the Web as a socially
embedded technology that influences and is influenced by society. The Web has
changed the nature of social interaction, business, education, politics. This module
provides a grounding in analytical techniques required to understand these changes
and gain insights into developing new opportunities. It introduces techniques for
analysing and modelling the Web from a semantic, structural and user-behaviour
perspective.
(brief description of the content of the module between 75 – 150 words)
*Note Field to indicate taught through Irish/English/Erasmus
Course Instances (s)
ME CS&IT
1SPE, 2SPE, 3SPE, 4SPE 1SPD,
2SPD, 3SPD, 4SPD
Module version number and date approved
xx/xx/2012
*
xx/xx/2012
xx/xx/2012
Date Retired
Module Owner / Lecturer
Module Administrator Details
Dr Conor Hayes
Ms Mary Hardiman, ext 3836
info@it.nuigalway.ie
Please specify main contact person(s) for exam related queries and contact number /email
Module Code
(
Module Type
Core= Student must take the module
Optional = Choice for Student
Office use only)
ECTS
Multiple of 5 ects
5 ects
Optional for
Core for
Course Requirement
(i.e. where a module has to be passed at 40%)
Semester Taught
Semester Examined
Semester 2
Requisite(s)
Semester 2
Co-Req.
Modules 
If they take module X they must
take module Y
Pre-Req
Modules 
The student must have taken and
passed a module in previous year
Excl.Req.
Modules 
If they take module X they
CANNOT take module Y
Module Assessment
st
1 Sitting
2nd Sitting
Assessment Type
Exam Session
Duration
Written Paper
Semester 2
2 Hours
Written Paper
Autumn
2 Hours
Bonded Modules
Draft Created by Syllabus Team as part of Academic Simplification 2012/2013
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(modules which are to be
examined at the same date and
time)
Draft Created by Syllabus Team as part of Academic Simplification 2012/2013
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PART B
Workload:
ECTS credits represent the student workload for the programme of study, i.e. the total time
the student spends engaged in learning activities. This includes formal teaching, homework,
self-directed study and assessment.
Modules are assigned credits that are whole number multiples of 5.
One credit is equivalent to 20-25 hours of work. An undergraduate year’s work of 60 credits is
equivalent to 1200 to 1500 hours or 40 to 50 hours of work per week for two 15 week
semesters (12 weeks of teaching, 3 weeks study and formal examinations).
Module Schedule
No. of Lectures Hours 24
No. of Tutorials Hours 12
No. of Labs Hours
Recommended No. of self study
hours 80
Other educational activities(Describe)
and hours allocated
Lecture Duration
Tutorial Duration
Lab Duration
Placement(s) hours
1 hr
1
*Total range of hours to be automatically totalled (min amount to be hit)
Module Learning Outcomes
(CAN BE EXPANDED)
On successful completion of this module the learner should be able to:
1 Understand the Web Science Paradigm; the challenges in maintaining the
Web, and its platform neutrality, the challenges posed from a technical,
social, political, and economic perspective
2Understand and be able to apply the core techniques in network
analysis and visualisation as applied to the link structure of the Web
and online social networks
3Understand and be able to apply core techniques in text extraction,
particularly as applied to topic modelling, key-phrase extraction and
relation annotation
4Understand be able to apply core techniques in user behaviour analysis
and modelling, as applied to users in online social systems
5Understand the limitations of the document-centric Web; understand and
be able to apply the principles of the Web of Linked Data
6Understand the challenges posed by Big Data and the state-of-the art
approaches in analysing Web Data in an efficient manner
7Understand the cross-disciplinary nature of Web Science – and the
perspectives offer by other disciplines on the Web and its future
8Understand practical aspects of Web Science and Analytics through use
cases studies
Module Learning, Coursework and Assessment
Learning Outcomes at module level should be capable of being assessed. Please indicate assessment methods and the outcomes they will assess
Assessment type, eg. End of year exam, group project
Written Paper
Continuous Assessment
Outcomes
assessed
% weighting
1-8
70
2,3,4,5
30
Indicative Content (Marketing Description and content)
Draft Created by Syllabus Team as part of Academic Simplification 2012/2013
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Definition of the Web Science Paradigm: understanding the Web as complex
system. Technical underpinning of current Web: the strengths,weaknesses
and challenges for the future; challenges posed to the Web from society:
economic, political, social perspectives. Network analysis techniques –
theoretical fundamentals of social network analysis and community
finding; Algorithms for centrality calculation, community-finding and
force-directed layouts. Content-based analyis techniques - theoretical
and applied fundamentals of NLP vs statistical techniques, as applied to
data on the Web – documents and informal text (twitter etc). User
behaviour analysis – fundamentals of user behaviour analysis; server–log
analysis; user interaction analysis; role-analysis; Overview of Web as a
Big Data and current approaches – e.g. stream analytics , hadoop.
Foundation of the Semantic Web and the challenges it addreses; Web
Science as an inter-disciplinary subject – links to sociology,
economics, politics, law
Module Resources
Suggested Reading Lists
Library
Reading will be assigned from a number of texts and
research papers
E.g. 'Web Data Mining: Exploring Hyperlinks, Contents,
and Usage Data', Bing Liu
'Social Network Data Analytics', Charu Agrawal
'Community Detection and Mining in Social Media', Lei
Tang, Huan Liu
Journal
Physical (e.g. AV’s)
IT (e.g. software + version)
Admin
FOR COLLEGE USE ONLY
Student Quota
Quota
(where applicable only)
(identify number per module where applicable only)
Module:
Number:
Discipline involved in Teaching
Share of FTE
*(drop down for disciplines within school)
Information Technology
*(% out of 1)
100%
RGAM
NB:
Notes on some fields are for the technical side when considering which
software company to use.
Draft Created by Syllabus Team as part of Academic Simplification 2012/2013
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