Program Overview

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Data Analytics (MSc)

Overview

Data Analytics is an exciting field of rapid developments. Data is everywhere and continuing to grow massively, creating huge growth in demand for qualified experts to be able to extract the real benefit from the data.

Course Structure

Modules are taught in block delivery mode where each module runs in blocks of 4 weeks in a sequential manner where at any one time, the student is working on only one module.

Course Details

The aim of the programme is to offer a multi-disciplinary education in data analytics that prepares graduates with key knowledge, skills and competencies necessary for employment in analytics and data science positions.

Modules

• Data Analytics Fundamentals
• Databases and Programming Fundamentals
• Data Mining
• Machine Learning
• Frontiers in Data Analytics
• Analytics in Action
• Individual Industry Based Project

Entrance Requirements

• Normally a 2.1 Honours degree in Mathematics, Statistics, or Computer Science or a closely related discipline, or equivalent qualification acceptable to the University.
• Applicants with a minimum 2.2 Honours degree in a cognate discipline, a 2.1 Honours degree in a non-cognate discipline, or who have not yet completed their degree, will be required to pass an aptitude test.

English Language Requirements

• Evidence of an IELTS* score of 6.5, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required.

Tuition Fees

• Northern Ireland (NI) £8,800
• Republic of Ireland (ROI) £8,800
• England, Scotland or Wales (GB) £9,250
• EU Other £25,800
• International £25,800

Career Prospects

Industry forecasts indicate that Data Analytics is a growing field internationally, with job opportunities set to increase exponentially predicting growths of 160% between 2013 and 2020.

Analytics in Action

Overview

Real data presents new challenges. Unlike the well “behaved” data often used as illustrative examples in text books, real data will require careful attention to ensure it is used correctly and that the correct approaches are applied to it.

Learning Outcomes

• Translation of real data into meaningful knowledge using analytics.
• Deep understanding of analytics to be able to select the most appropriate method for the situation being presented.
• Effective presentation and translation of the analytics results and impact on business to both experts and to non-experts.

Skills

• Successful participation in this module will enable students to develop skills in the following areas:
  • To evaluate a real life problem and make an informed judgement as to the most suitable analytics approach to take.
  • To implement the analytics approaches using real data.
  • To effectively present the results and recommendations of the analysis to expert and non-expert colleagues.

Individual Industry Based Project

Overview

The project will take the form of an extensive analytics investigation and be based in a local company.

Learning Outcomes

• On completion of this module, the successful student will have achieved the following learning outcomes, commensurate with module classification:
  • Deep knowledge and understanding of a given problem.
  • Critically evaluate an analytics problem.
  • Conduct a detailed analysis of the literature or previous case studies.

Skills

• Successful completion of this project module will enable students to develop skills in the following areas:
  • To evaluate a real life problem and make an informed judgement as to the most suitable analytics approach to take.
  • To implement the analytics approaches using real data.
  • To effectively present the results and recommendations of the analysis to expert and non-expert colleagues.

Frontiers in Analytics

Overview

The module highlights two state-of-the-art disciplines in the general field of analytics: Visual Analytics and Behavioural Analytics.

Learning Outcomes

• Comprehensive understanding of visual analytics as a science.
• Assess and interpret large, disparate data sets.
• Design and create bespoke interactive decision-making environments.

Skills

• TO BE ADDED

Machine Learning

Overview

This module introduces the basics of machine learning algorithms and how to implement them.

Learning Outcomes

• Comprehensive understanding of machine learning algorithms.
• Critical evaluation of when different algorithms are suitable.
• Practical techniques in data analytics, modelling and performance evaluation.

Skills

• The ability to use machine learning algorithms to make predictions on real data.

Database & Programming Fundamentals

Overview

The module will provide the basics of how to extract, store, manage, manipulate and integrate both big and small data using Python.

Learning Outcomes

• Understand fundamental concepts in programming such as variables, loops, logic and functions.
• Demonstrate knowledge and understanding of appropriate techniques in Python for building efficient programs.

Skills

• Demonstrate ability to design, develop, test and debug simple programs.

Data Mining

Overview

This module will introduce the basics of data mining and present the need for data mining approaches and how they can handle big data.

Learning Outcomes

• Comprehensive understanding of the concept of data mining and principal data mining algorithms.
• Understand the mathematical representation of the data mining approaches.

Skills

• The ability to use statistical data mining to model data, make predictions and recommendations from the models produced.

Data Analytics Fundamentals

Overview

This module will introduce data analytics and the basic approaches used to collect and investigate data in a meaningful way.

Learning Outcomes

• Knowledge and understanding of the concept of data analytics and predictive analytics.
• Knowledge and understanding of hypothesis testing.

Skills

• The ability to use statistical tools to assess data quality and distributional form and cleanse data.