New Module Form

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Draft Created by Syllabus Team as part of Academic Simplification

2012/2013

Page
1



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
perspecti
ve.

(brief description
of the
content of the module

between 75


150 words)

*Note Field to ind
icate

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

Dr Conor Hayes

Module Administrator Details

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




(
Office use only)

Module Type

Core= Student must take the

module


Optiona
l = Choice for Student



Optional for









Core for







ECTS

Multiple of 5 ects

5 ects

Course Requirement





































(i.e. where a module has to be passe
d at 40%)

Semester Taught


Semester 2

Semester Examined

Semester 2


Requisite(s)

Co
-
Req.

If they take module X they must
take module Y

Modules 




















Pre
-
Req


The student must have taken and
passed a module in previous year


Modules 













Excl.Req.

If they take module X they
CANNOT
take module Y


Modules 

























Module Assessment

1
st

Sitting


2
nd

Sitting

Assessment Type

Written Paper



Written Paper



Exam Session

Semester 2



Autumn



Duration

2 Hours



2 Hours





Bonded Modules





















Draft Created by Syllabus Team as part of Academic Simplification

2012/2013

Page
2


(modules which are to be
examined at the same date and
time)





















Draft Created by Syllabus Team as part of Academic Simplification

2012/2013

Page
3


PART B




















Module Schedule

No. of Lectures

Hours

24

Lecture Duration

1 hr


No. of Tutorials

Hours

12

Tutorial Duration

1


No. of Labs

Hours







Lab Duration







Recommended No. of self study
hours

80

Placement(s)

hours








Other educational
activities
(Describe)

and hours allocated








*
Total range
of hours to be automatically totalled (min amount to be hit)




Module Learning Outcomes


(
(
C
C
A
A
N
N


B
B
E
E


E
E
X
X
P
P
A
A
N
N
D
D
E
E
D
D
)
)


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

2
Understand 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

3
Under
stand and be able to apply core techniques in text extraction,

particularly as applied to topic modelling, key
-
phrase extraction and

relation annotation

4
Understand be able to apply
core techniques in user behaviour analysis

and modelling, as applied to users in online social systems

5
Understand the limitations of the document
-
centric Web; understand and

be able
to apply the principles of the Web

of Linked Data

6
Understand the challenges

posed by

Big Data and the state
-
of
-
the art

approaches in analysing Web Data in an efficient manner

7
Understand the cross
-
disciplinary nature of Web Science


and the

perspectives offer by other disciplines on the Web and its future

8
Understand practical aspects of Web Science and Analytics through use

cases studies


Module Learning, Coursework and
Assessment

L
L
e
e
a
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w
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Assessment type
,
,


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.


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Outcomes
assessed


%
%


w
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Written Paper

Continuous Assessment



1
-
8


2,3,4,5










70


30











Indicative Content

(Marketing Description and content)

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 undergra
duate 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).


Draft Created by Syllabus Team as part of Academic Simplification

2012/2013

Page
4


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. stre
am 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


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 Det
ection and Mining in Social Media
'
, Lei
Tang, Huan Liu

Library







Journal







Physical

(e.g. AV’s)







IT

(e.g. software + version)







Admin










FOR COLLEGE USE ONLY

Student Quota

(where applicable only)

Quota

(identify number per module where applicable only)

Module:






Number:






Discipline involved in Teaching

*
(drop down for disciplines within school)

Information Technology

Share of FTE

*
(% out of 1)

100%

RGAM



NB:

Notes on some fields are for the technical side when considering which
software

company to use.