Course Content 
Expand+MSc in Computing (Data Science) New!
The parttime online MSc in Data Science offered by IT Sligo prepares graduates for future work opportunities in data analytics, big data and artificial intelligence. Data Science is an occupation in high demand ...
HideMSc in Computing (Data Science) New!
The parttime online MSc in Data Science offered by IT Sligo prepares graduates for future work opportunities in data analytics, big data and artificial intelligence. Data Science is an occupation in high demand with strong employment growth across multiple industry sectors. Our unique Masters programme combines 6 taught modules with a strong research project, where students are given the opportunity to implement their learning within a reallife context.
Data Science is a multidisciplinary field that attempts to extract knowledge from structured and unstructured data. It combines techniques from mathematics, statistics, information theory, computer science and artificial intelligence with applications in a wide variety of fields.
The programme is delivered parttime online over two stages.
Stage one of the programme consists of six taught modules, delivered over three semesters. The taught modules examine key aspects of Data Science. These taught modules will provide the student with the fundamental skills required for solving problems in the area of Data Science. An additional taught module in Research Methods will also be provided to assist the students in beginning stage two.
Stage two consists of a research project which is designed to develop a real and tangible research project, relevant to academic requirements, the student’s career aspirations and/or employer’s needs. The student will complete the project over three semesters. Students will be able to propose their own project areas, subject to agreement with their supervisor. Work related projects are encouraged, again, subject to agreement with their supervisor.
The taught programme includes modules in the following areas:
Introductory Programming for Data Science
Programming for Data Science will introduce the learner to the core concepts of data science programming. The student will be introduced to the Python programming language (specifically SciPy) generally, and will employ functions to manipulate lists, before implementing multidimensional arrays using Numpy or similar in order to perform statistical operations and linear equations. The student will then manipulate data frames and timeseries data using pandas or similar. SQL programming will also be introduced. Finally, the student will create, populate and query a NoSQL cloud database.
Applied Statistics and Probability
This module covers the statistics and probability required for a MSc in Data Science. The learner will gain the expertise to interpret the probabilistic models used in the appropriate literature. It will cover statistical methods to analyse and quantify processes. It will enable learners to model problems using probabilistic and statistical mathematical methods.
Data Analytics and Visualisation
This module covers the data analysis and visualization skills required for a Masters in Data Science. This topic will introduce the learner to data analysis techniques, which helps to interpret and extract meaningful information from raw data. The learner will gain the expertise in data preprocessing, exploratory data analysis and visualization, pattern recognition and discriminative classification. The learner will work on solving realworld problems.
Applied Linear Algebra
The subject covers the linear algebra required for postgraduate engineering and computing courses. The learner will gain the expertise to interpret the linear algebra models used in the appropriate literature. It will also enable learners to model problems using linear algebra methods.
Machine Learning
This module introduces the topic of machine learning algorithms (algorithms that learn from data), with the first part of the module dedicated to the standard shallow forms of machine learning before moving on to Deep Learning and Convolutional Neural Networks for use in computer vision tasks, particularly recognition, classification and localisation. The emerging topic of Deep Reinforcement Learning will be briefly introduced. The module will look at training strategies and frameworks for Deep Learning. As well as the technical/scientific elements, students will reflect on the ethical implications of machine learning.
Programming for Big Data
This module introduces students to the architectures and tools underpinning the management and processing of large scale datasets, which are too big for conventional approaches. Students will understand these architectures and tools and be able to use them to code solutions, query data from structured, unstructured, and streamed sources, and analyse that data using appropriate algorithms.
Students will also be able to evaluate a variety of Big Data Cloud platform providers e.g. Amazon AWS, Microsoft Azure, in order to deploy and host data solutions.
