MSc Data Science

Postgraduate, Online learning

Data Science MSc



Develop data science skills and knowledge by building on existing expertise to drive the data science capability within your organisation

Overview

Data science is a major growth area within both the commercial and public sectors and there is a shortage of professionals that have the required range of data science knowledge and skills. This work-based learning MSc Data Science programme addresses this shortage.

The programme is aimed at those working in a data-related role within their organisation, whether in a technical, software or business context and want to enhance their skills and understanding of contemporary data analysis tools and techniques.

Edinburgh Napier University has excellent research and knowledge transfer links with many local, national and international organisations in data science related areas.

The acquisition of knowledge and skills on the programme will enable students to drive improvements within their organisations, enabling them to focus on areas of specialisation within their professional field and to broaden their knowledge and skills to enhance their career development.

Delivered online, this MSc is ideally suited to individuals who intend to balance their personal and professional commitments and study while working.

Students working in the Broadcast Newsroom at Merchiston Campus

Mode of Study:

Online learning (available as Part-time)

Duration:

2-3 years

Start date:

SepJanMay


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Course details

You will develop the business understanding and analytical, statistical and computing skills required to contribute to this vital field. Linking learning and development to your work activities, you can ensure that your professional development is part of the strategic plan of your organisation to promote innovation and change.

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.

Your final dissertation project will allow you to use the tools and approaches you’ve developed on the course.

  • calendar How you’ll be taught

    This course is delivered fully online and the online materials have been designed to support you to study at a place and pace which suits your needs.

    Your employer is required to provide support, and to give permission for you to use for academic credit purposes a project (or series of related projects) within your workplace, which are part of your planned work activity and undertaken during normal working hours.

Modules

Modules that you will study* as part of this course

Advanced Professional Practice ( SOC11807 )

Reflective practice – using different models and frameworks to maximise both personal and team performance
Career development through mentoring and subject specific skills development

Further information

Data Analytics ( SET11822 )

The aim of this module is to enable you to develop a deep understanding of the fundamentals of data analytics, and to give you opportunities to practise a set of popular data analytical tools. Topics covered include:

*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

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 and practical skills in deployment and use of tools and critical evaluation in addition to providing useful generic skills for employment.

Further information

Data Wrangling ( SET11821 )

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.

Further information

Data-Driven Decision Making ( INF11816 )

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. Topics covered include:
* 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.

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 and its relevance to everyday life, critical evaluation and professional considerations and practical skills in the deployment and use of tools and critical evaluation of complex problems in addition to providing useful generic skills for employment.

Further information

Masters Dissertation ( SOC11101 )

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
(2) Initial Report including time plan and dissertation outline

Further information

* These are indicative only and reflect the course structure in the current academic year. Some changes may occur between now and the time that you study.

Entry requirements

Entry requirements

We encourage you to submit your application for September 2018 entry by 31st July 2018.

The entry requirement for this course is a Bachelor (Honours) Degree at a 2:2 or above in an appropriate field, for example, software development, computing, or business analytics. Alternatively, other qualifications or experience that demonstrate through our recognition of prior learning process that you have appropriate knowledge and skills at SCQF level 10 may be considered.

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.

English language requirements

If your first language isn't English, you'll normally need to undertake an approved English language test and our minimum English language requirements will apply.

This may not apply if you have completed all your school qualifications in English, or your undergraduate degree was taught and examined in English (within two years of starting your postgraduate course). Check our country pages to find out if this applies to you.

International students

We welcome applications from students studying a wide range of international qualifications.
Entry requirements by country

Please note that non-EU international students are unable to enrol onto the following courses:
  • BN Nursing/MN Nursing (Adult, Child, Mental Health or Learning Disability)
  • BM Midwifery/MM Midwifery

Admissions policies

We’re committed to admitting students who have the potential to succeed and benefit from our programmes of study. 

Our admissions policies will help you understand our admissions procedures, and how we use the information you provide us in your application to inform the decisions we make.

Undergraduate admissions policies
Postgraduate admissions policies

Executive Masters

We run a suite of Executive Masters courses for organisations looking to upskill their staff and professionals looking to develop new skills and advance their career.

Fees & funding

The course fees you'll pay and the funding available to you will depend on a number of factors including your nationality, location, personal circumstances and the course you are studying. We also have a number of bursaries and scholarships available to our students.

Tuition fees
All Students 2018/19 2019/20
All students *£770 tba
Modules are purchased via our online store and paid for in full at time of enrollment.
This course comprises of 180 credits, therefore the total cost is currently £6,930.


Careers

Participation in this course will indicate your aspirations as a leading Data Scientist and your dedication to your management, enhancing your chances of promotion. 
Interior photo of atrium in Edinburgh Napier University Sighthill campus building.