MSc Data Science
The University has adopted "the Information Society" as one of three encompassing themes that provide a strategic context for cross-University activity and interdisciplinary research in areas of strength and external relevance.
The MSc Data Science Programme is key to expanding both research and teaching in the Information Society theme around using big data, analytics, and visualisation to shape the digital future, where the creation, distribution, use, integration and manipulation of data is a significant economic, political and cultural activity.
The areas of Data Science and associated technologies are in high demand both in the commercial and public sectors and there is thus a shortage of professionals that have the required range of Data Science understandings and skills. This programme addresses this shortage.
This programme is appropriate for you if you are working in a data-related role within your organisation, whether in a technical, software or business context and you want to enhance your skills and understanding of contemporary data analysis tools and techniques.
Data science is a major growth area within both the commercial and public sectors. The programme will enable practitioners with a background in business, quantitative science, or computing to develop into effective data scientists with the business understandings and analytical, statistical and computing skills to contribute to this vital field. Edinburgh Napier University has excellent research and knowledge transfer links with many local, national and international organisations in data science related areas. The programme that focuses on the particular requirements of your organisation and your own professional development needs in the area of Data Science.
The acquisition of knowledge and skills on the programme will enable students to drive improvements within their organisations. The programme also enables students to focus on areas of specialisation within their professional field and to broaden their knowledge and skills to enhance their career development.
Learning, teaching and assessment methods focus on providing students with engaging and contemporary materials that link theory to practice and require students to take a critical perspective on both. By linking learning and development directly to work activities, students can ensure that their professional development is part of the strategic aims of the organisation. Students will also have the opportunity to consider and reflect on established views of the organisation and processes relating to Data Science, in order to promote innovation and change.
This programme aims to:
- Provide students with a balance of technical, analytical and business skills related to data science.
- Engage students in the practical application of their developing knowledge and skills in data science to data-related problems in their organisations.
- Provide students with the opportunity to plan and conduct a structured work based learning activity and a dissertation related to data science.
- Stimulate an enquiring, analytical, creative and reflective approach that encourages independent judgement and critical awareness.
- Provide a platform for continuing personal and professional development.
|Modules (ALL COMPULSORY)|
|Trimester 1 (15 weeks)||Trimester 2 (15 weeks)||Trimester 3 (15 weeks)|
Data Driven Decision Making
Data Analysis and Visualisation
Advanced Professional Practice
|Trimesters 4 and 5 (15 weeks – 30 weeks)|
Data Driven Decision Making
In this module you will learn how organisations can use data to develop their organisation and enhance their understanding of how data is collected, analysed, and managed, not simply in terms of the tools required but also how data impacts on the people, processes, and technology used by the business. In addition you will learn a scientific approach to organise and interrogate data, the concepts of using statistical inference within decision-making, how to apply scientific principles in a systematic and methodological way to simplify complex data and use data to segment subjects in to homogeneous groups, including statistical modelling techniques. Emphasis is given to the presentation of data to facilitate understanding. The module also considers how these impact upon the decision-making process from a strategic and tactical perspective.
A primary use of data by contemporary organisations is to analyse and explore opportunities for growth or change, either directly or indirectly. The demand for business data, whether operational management, data analytics or data science (such as "big data", machine learning & predictive analytics) has increased substantially. This has resulted from an organisational need for a more sophisticated approach to analytics and data from both a business and statistical understanding of data and its impacts on the organisation. This raises complex and multifaceted issues.
The aim of the module is to enable you develop a deep understanding of the business context and impact of data, the meaning of the data (including in terms of statistics), and to give you an opportunity to express this in the form of professional written reports.
- The role of the data scientist
- Data strategy and Key Performance Indicators (KPIs)
- Deployment and implementation
- Governance, ethical and cultural implications
- Exploring and describing data,
- Statistical inference – parametric methods t – tests and Analysis of Variance Statistical presentation of data.
- Multivariate methods – principal component analysis, exploratory factor analysis and segmentation methods (Hierarchical clustering, K means and K modes).
- Statistical modelling – OLS regression, general linear models exemplified by Binary Logistic models
- Diagnosing model fits
The R package for statistics will be used in this module.
Data Analysis and Visualisation
In this module you will gain a detailed insight into the practical and theoretical aspects of topics in data analytics, such as Data Pre-processing, Data Modelling, Data Mining, and Data Visualisation. You will examine the fundamentals and advanced methods and techniques on these topics, and the use of data analytical tools.
- Data Pre-processing – data quality, data cleaning, data preparation
- Data Analytics – techniques of analysing data, such as classification, association, clustering and visualisation, including a variety of machine learning methods that are widely used in data mining
- Post processing – data visualisation, interpretation, evaluation
This module will use tools such as OpenRefine, Weka and Tableau for standard and structured data
This module concerns the processes, programming and tools necessary for the identification, interfacing, aggregation, and processing of mixed and unstructured data types from disparate data sets. The challenges of contemporary data acquisition and analysis have been characterised as "the four V’s of Big Data" (volume, variety, velocity and validity). These require the use of specialised data storage, aggregation and processing techniques. This module introduces a range of tools and techniques necessary for working with data in a variety of formats with a view to developing data driven applications. The module focuses primarily on developing applications using the Python scripting language and associated libraries and will also introduce a range of associated data storage and processing technologies and techniques.
The module covers the following topics:
- Data types and formats: numerical and time series, graph, textual, unstructured
- Data sources and interfaces: open data, APIs, social media, web-based
- NoSQL databases such as document (MongoDB), graph and key value pair
- Techniques for dealing with large data sets, including Map Reduce
- Developing Data Driven Applications in Python
The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools, Requirements Analysis and practical skills in specification, development and testing and the deployment and use of tools and critical evaluation in addition to providing useful generic skills for employment.
Advanced Professional Practice
This module allows you to develop specialist computing skills in your workplace. You will negotiate, with your line manager and a mentor, a learning agreement which will identify objectives. The objectives you identify, once agreed, will form the basis of a significant piece of work which will be based on a live workplace issue. This module is designed to develop critical reflective practice, specialist computing skills, and act as a focus for your continuous professional development. Reflective practice – using different models and frameworks to maximise both personal and team performance. Career development through mentoring and subject specific skills development.
In this 60 credit module you will take control of your studies to produce a substantial piece of focussed academic research. Success in the dissertation module indicates an ability to work independently, so you are expected to take the initiative and manage your own project. In effect, you are learning how to do research; a sort of apprenticeship to an experienced academic supervisor in some respects.
The work for this module comprises the completion of an individual research project. Each student is assigned a personal Supervisor, and an Internal Examiner who monitors progress and feedback, inputs advice, examines the dissertation and takes the lead at the viva. There are three preliminary deliverables prior to the submission of the final dissertation: (1) Project proposal (there is a formal approval process by module leader before projects start); (2) Initial Report; (3) Outline dissertation.
- Assessments are completed and submitted through the secure learning environment. The schedule for assessments is published in advance. There are no formal exams – assessments is by end of unit quizzes and end of module assignments.
- The MSc Data Science comprises of four modules and a dissertation to a timescale that matches your requirements, but can be completed in 18 months minimum/4 years max.
- Applicants will be expected to be working in a role related to data analytics, whether in a technical or business context and will be required to provide a letter of support from their employer. Some experience of associated technologies such as databases, software development and related tools is assumed.
- Applicants will preferably possess a Bachelor’s degree with honours in an appropriate field, i.e. software development, computing, or business analytics.
- Your application will be considered on an individual basis, taking into consideration your previous study and experience.