Data Science MSc

Belfast, Belfast, United Kingdom

Shortlist

Jan, SepStarting Month

Shortlist

Programme overview

Main Subject

Data Science and Artificial Intelligence

Degree

MSc

Study Level

Masters

Study Mode

On Campus

This intensive three semester specialist masterâ€™s course in Data Science is aimed at highly-motivated graduates with a good honours degree in computing, engineering or a related discipline. While the course has a particular focus on the employment needs of the local economy, the skills and abilities developed are easily transferred to a more global stage. A major challenge for companies is attracting and retaining the right talentâ€”not only data scientists but business translators who combine data savvy with industry and functional expertise. The science of extracting information from data continues to increase in importance in various disciplines in which the large volume and complexity of the data imposes unprecedented challenges to the data analysis approaches traditionally employed in these disciplines. This course enables graduates to embark on a professional career in the general area of data science with the high level data analytics skills needed to contribute to this rapidly changing marketplace. Modules Here is a guide to the subjects studied on this course. Courses are continually reviewed to take advantage of new teaching approaches and developments in research, industry and the professions. Please be aware that modules may change for your year of entry. The exact modules available and their order may vary depending on course updates, staff availability, timetabling and student demand. Data Science Foundations: The focus of this module is to present an understanding of key data science concepts, tools and programming techniques. Within the arena of data science, the theory behind the approaches of statistics, modelling and machine learning will be introduced emphasising their importance and application to data analysis. The notion of investigative and research skills will also be introduced through a number of problem-solving exercises. The material covered will be contextualised by providing examples of the latest research within the area. Students will also be introduced to programming with Python. They will learn the basics of syntax, and how to configure their development environment for the implementation and testing of algorithms related to data science. Big Data Technologies: Within this module a variety of database and data storage paradigms will be explored, ranging from more traditional relational systems to NoSql and object stores, time series databases and graph stores. Consideration will be given to big data and the problem with storing and querying high volumes of highly variable data which is stored and processed at a high speed. The cloud computing paradigm will also be introduced and how to avail of its power and resources. The core concepts of distributed computing will be examined in the context of Hadoop and Spark. Students will be taught, practically and theoretically, about the components of Hadoop and Spark workflows, functional programming concepts and use of MapReduce. Business Intelligence and Analytics: This module aims to contextualise the role of Business Intelligence and Business Analytics and why we need them. A particular focus will be on how to turn already stored data into valuable information and why this is important. For instance, vast amounts of data regarding company's customers and operations is routinely collected and stored in large corporate data warehouses. This data can be of immense value if properly analysed. Students will explore techniques and tools for data analysis, and presentation of the results to non-technical and managerial staff, in alignment with business strategies. Business intelligence and analytics however, are open to certain ethical and consent issues along with risks. These will be analysed, reviewed and evaluated. Data Validation and Visualisation : High-quality data is the precondition for analysing and using big data and for guaranteeing the value of the data. This module, introduces the data quality challenges faced by big data. It will present tools and techniques employed to ensure data quality from data collection and computational procedures to facilitate automatic or semi-automatic identification and elimination of errors in large datasets. The module also introduces the topic of understanding and interpreting data through descriptive statistical methods. This will be achieved through a range of techniques such as Statistical metrics, Univariate analysis and Multivariate analysis. Students will develop the knowledge to assess the quality of the data and the skills necessary to perform appropriate data cleaning operations. In addition, students will have an understanding of processing data and interpreting and visualising results. Deep Learning and Natural Language Processing: Deep Learning and Natural Language Processing (NLP) are some of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP. Topics covered will include word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some very novel models involving a memory component. Students will learn the necessary engineering tricks for making neural networks work on practical problems. Statistical Modelling and Machine Learning: This module first provides a systematic understanding to probability and statistics. It then provides an in depth analysis of the statistical modelling process and how to answer hypothesised questions. Next, the module provides a synthesis of the concepts of data mining and methods of exploring data. The content will be delivered and experienced through lectures, seminars and practical exercises using tools, such as, Python, R and Weka. On completing this module, students will be able to compute conditional probabilities and use null hypothesis significance testing to test the significance of results, and understand compute statistical measures such as the p-value for these tests. Students

Programme overview

Main Subject

Data Science and Artificial Intelligence

Degree

MSc

Study Level

Masters

Study Mode

On Campus

Admission Requirements

We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the Universityâ€™s General Entrance Requirements. Entry Requirements Applicants must: (a) have gained (i) a second class honours degree or better, in the subject areas of computing, engineering or related discipline, from a university of the United Kingdom or the Republic of Ireland, or from a recognised national awarding body, or from an institution of another country which has been recognised as being of an equivalent standard; or (ii) an equivalent standard (normally 50%) in a Graduate Diploma, Graduate Certificate, Postgraduate Certificate or Postgraduate Diploma or an approved alternative qualification; and the qualification must be in the subject areas of computing, engineering or related discipline and (b) provide evidence of competence in written and spoken English (GCSE grade C or equivalent). In exceptional circumstances, as an alternative to (a) (i) or (a) (ii) and/or (b), where an individual has substantial and significant experiential learning, a portfolio of written evidence demonstrating the meeting of graduate qualities (including subject-specific outcomes, as determined by the Course Committee) may be considered as an alternative entrance route. Evidence used to demonstrate graduate qualities may not be used for exemption against modules within the programme. English Language Requirements English language requirements for international applicants The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement for Tier 4 visa purposes. Ulster recognises a number of other English language tests and comparable IELTS equivalent scores. English language requirements Your country Exemptions and transferability The entry requirements facilitate accreditation of prior learning.

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Belfast, Belfast, United Kingdom

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