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 a minimum of 20 credits in total 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 4

Please ensure that you register to a minimum of 5 credits in total from the option list below.



Note: In order to complete your Undergraduate degree, you must earn at least 40 credits at level 3 or above. It is your responsibility to ensure that you meet this requirement. Note: You may have taken some level 3 modules in stage 3 already.

Module ID Module Title Trimester Credits
Stage 1 Core Modules
     
COMP10010 Introduction to Programming I Autumn 5
CSOC10010 Introduction to Computational Social Science 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. Additional options can be selected in place of electives.
     
GEOG10140 Mapping a Sustainable World Autumn 5
PHIL10160 Critical Thinking Autumn 5
SOC10110 Sociology of Crime & Deviance Autumn 5
PHIL10040 Introduction to Ethics Autumn and Spring (separate) 5
IS10040 Information, Society, and Culture Spring 5
IS10060 Digital Technology Spring 5
SOC10060 Ireland in Comparative Perspective Spring 5
Stage 1 Options - A)MIN1OF:
Select a minimum of 1 of the following option modules. Additional options can be selected in place of electives.
     
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
     
CSOC20010 Applied Computational Social Science Autumn 5
MST10010 Calculus I Autumn 5
STAT20200 Probability Autumn 5
COMP10020 Introduction to Programming II Spring 5
CSOC30030 Advanced Computational Social Science 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.
     
COMP20070 Databases and Information Systems I Autumn 5
GEOG20220 Introduction to GIS for the Social Sciences Autumn 5
IS20010 Core Competencies for Digital Citizenship Autumn 5
IS20140 Exploring Text with Python Autumn 5
PHIL20490 Knowledge & Scepticism Autumn 5
PHIL20630 Art and Society Autumn 5
COMP20200 UNIX Programming Spring 5
COMP20280 Data Structures Spring 5
IS20030 Contextual Design Inquiry 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
PHIL20020 Logic Spring 5
PHIL20240 Applied Ethics Spring 5
PHIL20640 Philosophy of Mind Spring 5
SOC20330 Sociology of Peace, Conflict & Justice 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 - B)MIN20CR:
Please ensure that you register to a minimum of 20 credits from the option list below. Please note that there are 5 credit and 10 credit modules available. If you are studying abroad (Erasmus/non EU exchange) and/or undertaking an internship, please see registration guidelines here. Additional options can be selected in place of electives. If you are interested in taking IS30410 Web UX Evaluation, please review the module descriptor and contact the module coordinator directly (Judith.Wusteman@ucd.ie)
     
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
IS20140 Exploring Text with Python Autumn 5
IS30020 Web Publishing Autumn 5
IS30050 Information Architecture: Designing the Web Autumn 5
IS30350 The Digital Self Autumn 5
IS30460 Gender, race and diversity in the digital age Autumn 10
PHIL20320 Philosophy of Science Autumn 5
PHIL20490 Knowledge & Scepticism Autumn 5
SSCI30050 Digital Research Skills for the Social Sciences Autumn 5
STAT20240 Predictive Analytics Autumn 5
STAT30010 Time Series Autumn 5
COMP20090 Introduction to Cognitive Science Spring 5
COMP20200 UNIX Programming Spring 5
COMP20280 Data Structures Spring 5
COMP20290 Algorithms Spring 5
COMP30110 Spatial Information Systems Spring 5
ENG32080 Social Networks in Fiction: from Jane Austen to Conan Doyle Spring 10
IS20110 Social Media & Participation in an Online World Spring 5
IS20120 Computer-Mediated Communication Spring 5
IS20130 Social Studies of ICTs Spring 5
IS30370 Digital Media Ethics (formerly Information Ethics) Spring 5
IS30380 Digital Storytelling Spring 5
IS30410 Web UX Evaluation Spring 10
IS30450 Artificial Intelligence Spring 5
IS30470 Technology and Human Rights Spring 5
IS30480 Digital Media & Climate Crisis Spring 5
MST20050 Linear Algebra II Spring 5
PHIL20020 Logic Spring 5
PHIL20240 Applied Ethics Spring 5
PHIL20640 Philosophy of Mind Spring 5
SOC30220 Science and Society Spring 5
SSCI20030 Building Resilience Spring 5
STAT20180 Introduction to Bayesian Analysis Spring 5
STAT30270 Statistical Machine Learning Spring 5
Stage 3 Options - B)MIN20CR:
Please ensure that you register to a minimum of 20 credits from the option list below. Please note that there are 5 credit and 10 credit modules available. If you are studying abroad (Erasmus/non EU exchange) and/or undertaking an internship, please see registration guidelines here. Additional options can be selected in place of electives. If you are interested in taking IS30410 Web UX Evaluation, please review the module descriptor and contact the module coordinator directly (Judith.Wusteman@ucd.ie)
     
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:
Please ensure that you register to a minimum of 5 credits from the option list below. Additional options can be selected in place of electives.
     
COMP20070 Databases and Information Systems I Autumn 5
COMP20250 Introduction to Java Autumn 5
COMP30030 Introduction to Artificial Intelligence Autumn 5
IS30020 Web Publishing Autumn 5
IS30050 Information Architecture: Designing the Web Autumn 5
IS30350 The Digital Self Autumn 5
PHIL20490 Knowledge & Scepticism Autumn 5
STAT20240 Predictive Analytics Autumn 5
STAT30010 Time Series Autumn 5
COMP20200 UNIX Programming Spring 5
COMP30110 Spatial Information Systems Spring 5
CSOC30030 Advanced Computational Social Science Spring 5
GEOG30880 Projects in GIS Spring 5
IS20110 Social Media & Participation in an Online World Spring 5
IS20120 Computer-Mediated Communication Spring 5
IS20130 Social Studies of ICTs Spring 5
IS30370 Digital Media Ethics (formerly Information Ethics) Spring 5
IS30380 Digital Storytelling Spring 5
IS30450 Artificial Intelligence Spring 5
IS30470 Technology and Human Rights Spring 5
IS30480 Digital Media & Climate Crisis Spring 5
PHIL20640 Philosophy of Mind Spring 5
STAT20180 Introduction to Bayesian Analysis Spring 5
STAT30270 Statistical Machine Learning Spring 5
Stage 4 Options - B)MIN1OF:
Please ensure that you register to a minimum of 5 credits from the option list below. Additional options can be selected in place of electives.
     
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)
Stage 4 Options - Subject / Streams
     
See the UCD Assessment website for further details

Module Weighting Info  
  Award GPA
Programme Module Weightings Rule Description Description >= <=
BHSOC010 Stage 4 - 50.00%
Stage 3 - 30.00%
Stage 2 - 20.00%
Standard Honours Award First Class Honours

3.68

4.20

Second Class Honours, Grade 1

3.08

3.67

Second Class Honours, Grade 2

2.48

3.07

Pass

2.00

2.47


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