Computational Social Science (SCS1)

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In this programme we value undergraduate training which equips students with a clear understanding of the interdisciplinary challenges of the field of computational social science. In particular this new UCD BSc degree in Computational Social Science will enable students, whose orientation is towards the social sciences, to couple these core interests with their strong aptitude for computational and analytical training.



We value and support our students' entrepreneurial and innovative strengths as we understand that these characteristics will enable students to make the most of training on this programme. UCD graduates of the new BSc in Computational Social Science will be very well placed to avail of the many new and exciting career opportunities emerging as part of the data revolution impacting on our society and economy. In particular our core modules will encourage students to explore how computational social science can provide new insights and enrich our understanding of observed and new or emergent social phenomena.



In the programme we value training which is both pragmatic and professional in its orientation. We aim to encourage and support students to uphold the highest standards of data management and application in their work, both on and off campus. Students will be encouraged to explore opportunities available to combine their academic training with opportunities to gain valuable career-oriented skills and professional experience. We can also facilitate students who may want to focus on a much deeper academic engagement through advanced and more specialised modules also available.


1 - Demonstrate a broad understanding of the social sciences and their multidisciplinary nature.
2 - Demonstrate a core knowledge and understanding of the fundamentals of computational social science.
3 - Synthesise, evaluate, interpret and report theories and evidence in an open, analytical and critical manner.
4 - Apply problem solving skills in a variety of different contexts
5 - Apply appropriate computational social science and social data analytic techniques to address domain specific research problems.
6 - Work effectively and responsibly, using professional techniques, tools and technologies, as an individual and in teams.
7 - Demonstrate best practice in the collection, manipulation and storage of data, some of which may be sensitive; Responsible and professional use of data and reporting of research results.
8 - Demonstrate an awareness of the regulatory governance framework and principles of ethical practice in the social sciences and how these principles should be applied.
9 - Communicate effectively using written, quantitative, visual and oral method.
10 - Discuss, present and communicate their research ideas, data and results within a group setting and in one-to-one communication.
11 - Demonstrate awareness and aptitude to engage in ongoing training and upskilling
12 - Recognize and appreciate that the field of computational social science and social data analytics is a fast developing science wherein new technologies and analytical techniques are emerging rapidly and professional norms require engagement with this process.

This degree is designed to prepare students for employment in companies such as Google or Facebook, but also for work in traditional sectors where the analysis of social data is becoming more and more important. Over the next few years, there will be an increasing demand for graduates who can combine social sciences training with analytical and programming skills. It also leads to a range of graduate study opportunities in social sciences, social data analytics, statistics or computer science.  


Stage 3

Please ensure that you register to 50 credits in total for Stage 3. Select a minimum of 20 credits from the option list below, and 15 credits from each of your chosen subject stream option lists. If you are studying abroad (Erasmus/non EU exchange) and/or undertaking an internship, please see registration guidelines here.

Module ID Module Title Trimester Credits
Stage 1 Core Modules
     
COMP10010 Introduction to Programming I Autumn

5

CSOC10010 Introduction to Computational Social Science I Autumn

5

MST10030 Linear Algebra I Spring

5

STAT10060 Statistical Modelling Spring

5

Stage 1 Core Modules
     
Stage 1 Options - A)MIN1OF:
Select a minimum of 1 of the following option modules. Please note that MST00050 is compulsory for those without a H4 grade or above in Leaving Certificate Mathematics.
     
GEOG10140 Mapping a Sustainable World Autumn

5

IS10010 Information & Social Media Autumn

5

IS10050 Digital Judgement: Truth, Lies, & the Internet Autumn

5

MST00050 Mathematics: An introduction Autumn

5

PHIL10160 Critical Thinking Autumn

5

PHIL10040 Introduction to Ethics Autumn and Spring (separate)

5

DSCY10090 The Art of Living Well Spring

5

IS10040 Information, Society, and Culture Spring

5

IS10060 Digital Technology Spring

5

SOC10060 Ireland in Comparative Perspective Spring

5

SOC10100 Sociology of Human Rights Spring

5

Stage 1 Options - A)MIN1OF:
Select a minimum of 1 of the following option modules. Please note that MST00050 is compulsory for those without a H4 grade or above in Leaving Certificate Mathematics.
     
Stage 1 Options - Subject / Streams
     
Economics (BSc Computational Social Science)

Geography (BSc Computational Social Science)

Politics (BSc Computational Social Science)

Sociology (BSc Computational Social Science)

Stage 1 Options - Subject / Streams
     
Stage 2 Core Modules
     
MST10010 Calculus I Autumn

5

STAT20110 Probability Theory Autumn

5

COMP10020 Introduction to Programming II Spring

5

CSOC20010 Introduction to Computational Social Science II Spring

5

STAT20100 Inferential Statistics Spring

5

Stage 2 Core Modules
     
Stage 2 Options - B)MIN0OF:
Students may select an option module from the following list in place of an elective.
     
COMP20020 Digital Systems Autumn

5

COMP20070 Databases and Information Systems I Autumn

5

COMP20110 Discrete Mathematics for Computer Science Autumn

5

COMP20250 Introduction to Java Autumn

5

GEOG20220 Introduction to GIS for the Social Sciences Autumn

5

IS20010 Core Competencies for Digital Citizenship Autumn

5

IS20020 Information Modelling Autumn

5

IS20130 Social Studies of ICTs Autumn

5

PHIL20020 Logic Autumn

5

PHIL20630 Art and Society Autumn

5

SOC20220 Social Theory and Social Media Autumn

5

SOC20360 Sociology of the Environment Autumn

5

COMP20030 Web Design Spring

5

COMP20090 Introduction to Cognitive Science Spring

5

COMP20200 UNIX Programming Spring

5

COMP20280 Data Structures Spring

5

COMP20290 Algorithms Spring

5

IS20030 Information & Collaboration in Organisations Spring

5

IS20110 Social Media & Participation in an Online World Spring

5

IS20120 Computer-Mediated Communication Spring

5

MST20050 Linear Algebra II Spring

5

PHIL20240 Applied Ethics Spring

5

SOC20210 Animals and Human Society Spring

5

SOC20330 Sociology of Peace, Conflict & Justice Spring

5

SOC20350 Sociology of Law Spring

5

Stage 2 Options - B)MIN0OF:
Students may select an option module from the following list in place of an elective.
     
Stage 2 Options - Subject / Streams
     
Economics (BSc Computational Social Science)

Geography (BSc Computational Social Science)

Politics (BSc Computational Social Science)

Sociology (BSc Computational Social Science)

Stage 2 Options - Subject / Streams
     
Stage 3 Options - A)MIN3OF:
Please ensure that you register to a minimum of 20 credits from the option list below. If you are studying abroad (Erasmus/non EU exchange) and/or undertaking an internship, please see registration guidelines here.
     
COMP20020 Digital Systems Autumn

5

COMP20070 Databases and Information Systems I Autumn

5

COMP20110 Discrete Mathematics for Computer Science Autumn

5

COMP20250 Introduction to Java Autumn

5

COMP30030 Introduction to Artificial Intelligence Autumn

5

GEOG20220 Introduction to GIS for the Social Sciences Autumn

5

IS20010 Core Competencies for Digital Citizenship Autumn

5

IS20020 Information Modelling Autumn

5

IS20130 Social Studies of ICTs Autumn

5

IS30020 Web Publishing Autumn

5

IS30050 Information Architecture: Designing the Web Autumn

5

IS30380 Digital Storytelling Autumn

5

PHIL20020 Logic Autumn

5

STAT20110 Probability Theory Autumn

5

STAT30010 Time Series Autumn

5

STAT30240 Predictive Analytics I Autumn

5

IS30420 Soc Sc Research Accelerator Autumn and Spring (separate)

5

COMP20030 Web Design Spring

5

COMP20090 Introduction to Cognitive Science Spring

5

COMP20170 Introduction to Robotics Spring

5

COMP20200 UNIX Programming Spring

5

COMP20280 Data Structures Spring

5

COMP20290 Algorithms Spring

5

COMP30110 Spatial Information Systems Spring

5

IS20110 Social Media & Participation in an Online World Spring

5

IS20120 Computer-Mediated Communication Spring

5

IS30350 The Digital Self Spring

5

IS30370 Information Ethics Spring

5

MST20050 Linear Algebra II Spring

5

PHIL20240 Applied Ethics Spring

5

STAT20180 Bayesian Analysis Spring

5

STAT30270 Statistical Machine Learning Spring

5

Stage 3 Options - A)MIN3OF:
Please ensure that you register to a minimum of 20 credits from the option list below. If you are studying abroad (Erasmus/non EU exchange) and/or undertaking an internship, please see registration guidelines here.
     
Stage 3 Options - Subject / Streams
     
Economics (BSc Computational Social Science)

Geography (BSc Computational Social Science)

Politics (BSc Computational Social Science)

Sociology (BSc Computational Social Science)

Stage 3 Options - Subject / Streams
     
Stage 4 Core Modules
     
POL30660 Data Analytics for Social Sciences Spring

10

SOC30380 Social Dynamics and Networks Spring

5

Stage 4 Core Modules
     
Stage 4 Options - B)MIN1OF:
Select a minimum of 35 credits from the following option modules.
     
COMP2002J Data Struc and Algorithms 1 Autumn

5

COMP20070 Databases and Information Systems I Autumn

5

COMP20250 Introduction to Java Autumn

5

COMP30030 Introduction to Artificial Intelligence Autumn

5

IS20130 Social Studies of ICTs Autumn

5

IS30020 Web Publishing Autumn

5

IS30050 Information Architecture: Designing the Web Autumn

5

IS30380 Digital Storytelling Autumn

5

STAT20110 Probability Theory Autumn

5

STAT30010 Time Series Autumn

5

STAT30240 Predictive Analytics I Autumn

5

COMP20030 Web Design Spring

5

COMP20170 Introduction to Robotics Spring

5

COMP20200 UNIX Programming Spring

5

COMP30110 Spatial Information Systems Spring

5

IS20110 Social Media & Participation in an Online World Spring

5

IS20120 Computer-Mediated Communication Spring

5

IS30350 The Digital Self Spring

5

IS30370 Information Ethics Spring

5

STAT20180 Bayesian Analysis Spring

5

STAT30270 Statistical Machine Learning Spring

5

Stage 4 Options - B)MIN1OF:
Select a minimum of 35 credits from the following option modules.
     
Stage 4 Options - Subject / Streams
     
Economics (BSc Computational Social Science)

Geography (BSc Computational Social Science)

Politics (BSc Computational Social Science)

Sociology (BSc Computational Social Science)


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