M.Sc. Computer Science – Data Science

Source: https://teaching.scss.tcd.ie/m-sc-computer-science-data-science/ Parent: https://teaching.scss.tcd.ie/module/stu22005-applied-probability-ii/

Data Science – Core Modules

(Semester 1, 5 ECTS) Understand what machine learning is and how it works.

(Semester 1, 5 ECTS) Locate, obtain and critique relevant knowledge and evidence to support innovation and research

(Semester 3, 30 ECTS) Engage in a sustained piece of individual, academic research on a\ chosen topic within the field of computer science.

(Semester 2, 5 ECTS) The aims of this module are to give the student skills to model, analyse and solve optimisation problems that arise in data analytics and modern computing and communication systems.

(Semester 2, 5 ECTS) This module continues on from CS7CS4 (Machine Learning) with a focus on sampling methods and topical applications.

(Semester 2, 5 ECTS) The objectives of this module are: to develop an in-depth understanding of risk, data\ privacy, threats and risks of security breaches, an awareness of computer security\ (cryptographic) and protocol techniques, and an ability to make appropriate\ decisions about securing data.

(Semester 1, ECTS 5) This module aims to equip the student with the knowledge and tools to visualise data in ways that give insight and understanding.

(Semester 1 & 2, 10 ECTS) To understand the theory and be able to apply the following techniques\ to a set of data.

(Semester 1, 5 ECTS) This module aims to provide a theoretical and practical understanding of modern scalable systems and architectures, from billions of highly distributed Internet of Things devices, through to present and future concepts, such as Quantum and Nanotech systems.

Data Science – Elective Modules

(Semester 1 & 2, 10 ECTS) Assess the theory of classic architecture principles and apply an appropriate architectural model in a team-based application under development

(Semester 2, 5 ECTS) The aim of this module is to provide students with a deep understanding of the theory and techniques behind real time animation.

(Semester 2, 5 ECTS) Appreciate the scope, applications and limitations of artificial intelligence;

(Semester 1, 5 ECTS) Explain the process of content indexing in information retrieval including stop word removal, conflation (stemming, string-comparison), and the language dependency of these methods.

(Semester 2, 5 ECTS) In this module, students will explore the prevailing vision for an Internet of Things in\ a practical, pragmatic manner.

(Semester 1, 5 ECTS) This module aims to provide both a theoretical and practical understanding of\ modern and next generation networking and systems concepts, principles, practices\ and technologies. Contemporary and emerging wired and wireless network systems\ are targeted.

(Semester 1, 5 ECTS) Wave equation and its solution; Maxwell´s equations; Fourier transform and analysis; vibration; mass-spring-damper systems; numerical methods; simulation software.

(Semester 1, 5 ECTS) This module aims to provide both a theoretical and practical understanding of urban\ computing and associated cyber-physical concepts, principles, challenges and\ solutions.

(Semester 2, 5 ECTS)\ This course covers fundamentals and state-of-the-art in augmented reality, as well\ as related areas of 3D computer vision and graphics.

(Semester 2, 5 ECTS) Grasp the scope and limitations of finite state methods in text analysis.

(Semester 2, 5 ECTS) User modelling, including Task modelling\ User preferences

(Semester 2, 5 ECTS) This module deals with programming for GPU pipeline architectures e.g. geometry,\ rasterisation, texturing, fragment / pixel and vertex shaders.

(Semester 1, 5 ECTS) Image processing, feature detection and matching, image registration, recognition\ and segmentation – Motion flow and object tracking in video – Mathematics for\ computer vision.

(Semester 1, 5 ECTS) An introduction to computer graphics; problem domain and applications.

(Semester 1, 5 ECTS) The module is designed to explore the management, delivery and inter-operability of knowledge, information and data through knowledge and data engineering.

(Semester 2, 5 ECTS) This course takes a critical look at some of the architectural issues involved in, and paradigms available for, the construction of large-scale distributed systems such as the infrastructures supporting Google’s search engine or Amazon’s online sales platform. In particular, the course considers how to develop systems that must make trade-offs between performance, consistency, reliability, and availability.