Computer Science with Data Science (CSSC)

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This Programme is aimed at students who wish to develop a career or pursue further studies in Computer Science and Data Science. We value and therefore encourage our students to be active, motivated, autonomous learners who have a critical and reflective approach to Computer Science and Data Science. We aim to provide a learning environment that will encourage students to learn and practice skills in Computer Science and data analytics, individually and as part of teams. Practicals, tutorials and assignments are key elements of the design of the Programme. As part of this approach to learning, the Programme uses teaching, learning and assessment approaches such as tutorials, practicals, assignments and individual and team projects, as well as traditional lectures, in the design and delivery of the curriculum.


1 - Demonstrate understanding of specific bodies of knowledge within the disciplines of Computer Science and Data Science relating to the manipulation and preprocessing of large volumes of data and the statistical analysis of data.
2 - Develop new insights from the analysis of data.
3 - Have a broad awareness of related bodies of knowledge in Computer Science outside data analytics for example, computer networks, information security and software engineering.
4 - Make use of the insights and findings of research in Computer Science to inform their understanding of the field and how they operate within it.
5 - Work with established statistical and engineering methods in analysing data.
6 - Present their work in a public forum and communicate it to technical and non-technical audiences.
7 - Work as an individual and as a team member.
8 - Learn to work at varying scales and with projects of increasing complexity.
9 - Apply lessons learned in lectures, tutorials and practicals to develop the practice of "learning by doing", made evident in their assignments and project work and problem-solving strategies.
10 - Implement computer programs in a variety of programming languages and analyse and reason about these programs.
11 - Demonstrate awareness of issues (technical, financial, societal and ethical) in the areas of Computer Science and Data Analytics.
Approved Additional Standards for Continuation in undergraduate degree programmes in Science (all majors):

Students who return failing grades in a semester amounting to 15 credits, or more, will be identified under the UCD Continuation and Readmission Policy. Students whose rate of progression and performance over two academic sessions (2 years) is deemed unacceptable will be referred to the Academic Council Committee on Student Conduct and Capacity for exclusion from the programme.

As Stages 3 and 4 have the most dynamic components of the programme, and the material studied previously may no longer be relevant, a student who has been away from the programme for a significant period should be required to register again to Stage 3. The upper limit for completion of Stages 3 and 4 should be six years, if they choose to do 120 credits with 20 in each year.
Stage 3

Students take 7 core modules and 1 15-credit option module. Students take a further 10 credits as electives. Electives may also be chosen from within the BSc Programme.

Stage 4

Students take 6 core modules (40 credits) and 4 Option modules (20 credits).

Module ID Module Title Trimester Credits
Stage 3 Core Modules
     
COMP30030 Introduction to Artificial Intelligence Autumn

5

COMP30040 Networks and Internet Systems Autumn

5

COMP30760 Data Science in Python - DS Autumn

5

STAT20110 Probability Theory Autumn

5

COMP30750 Information Visualisation -DS Spring

5

COMP30770 Programming for Big Data Spring

5

COMP30850 Network Analysis Spring

5

Stage 3 Core Modules
     
Stage 3 Options - A)1 OF:
All students should select COMP30780 at the start of the academic year. Students who wish to apply for the Industry Internship module and are successfully placed on an internship will be manually registered by the School Office to COMP30790 and subsequently dropped from COMP30780. Further information is available at: http://www.ucd.ie/science/careers/internships/students/ COMP30920 is only offered if students are unable to complete their internships due to unforeseen events. The module will be registered by the School office if such events arise. This module is completed in the summer trimester.
     
COMP30790 Industry internship 2 Trimester duration (Spr-Sum)

15

COMP30780 Data Science in Practice Spring

15

COMP30920 Software & Data Project Summer

15

Stage 3 Options - A)1 OF:
All students should select COMP30780 at the start of the academic year. Students who wish to apply for the Industry Internship module and are successfully placed on an internship will be manually registered by the School Office to COMP30790 and subsequently dropped from COMP30780. Further information is available at: http://www.ucd.ie/science/careers/internships/students/ COMP30920 is only offered if students are unable to complete their internships due to unforeseen events. The module will be registered by the School office if such events arise. This module is completed in the summer trimester.
     
Stage 4 Core Modules
     
COMP30520 Cloud Computing (UG) Autumn

5

COMP30900 Final Year Project Foundations Autumn

5

COMP40370 Data Mining Autumn

5

COMP47750 Machine Learning with Python Autumn

5

COMP30910 FYP: Design and Implementation Spring

10

COMP47580 Recommender Systems & Collective Intelligence Spring

5

STAT30280 Adv Data Analytics (online) Spring

5

Stage 4 Core Modules
     
Stage 4 Options - A)MIN4OF:
Students must select 4 option modules from the list below.
     
COMP30190 Program Construction II Autumn

5

COMP30220 Distributed Systems Autumn

5

COMP30240 Multi-Agent Systems Autumn

5

COMP30250 Parallel and Cluster Computing Autumn

5

COMP30690 Information Theory Autumn

5

SCI30080 Professional Placement-Science Autumn

5

STAT30240 Predictive Analytics I Autumn

5

COMP30110 Spatial Information Systems Spring

5

COMP30230 Connectionist Computing Spring

5

COMP30540 Game Development Spring

5

COMP30720 Ethical Computer Hacking Spring

5

COMP40010 Performance of Computer Systems Spring

5

COMP40020 Human Language Technologies Spring

5

COMP40660 Advances in Wireless Networking Spring

5

COMP47390 Mobile App Dev - Cocoa Touch Spring

5

COMP47480 Contemporary Software Development Spring

5

COMP47650 Deep Learning Spring

5

COMP47660 Secure Software Engineering Spring

5

COMP47680 Human Computer Interaction Spring

5

IS30370 Information Ethics Spring

5

MATH30250 Cryptography: Theory & Practice Spring

5


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