MSc Data Science

Postgraduate, Part-time

Develop your Data Science related skills and knowledge - building on existing expertise, and driving forward the Data Science capability within your organisation.

  • Napier code:

    56750MM

  • Course type:

    Part-time

  • Duration:

    20 months

  • Award:

    MSc

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Course introduction

This course 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.

It is a flexible part-time programme aimed at existing professionals, providing a framework that allows you to focus on the particular requirements of your own organisation and your individual professional development needs.  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.

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


software engineering

You'll 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.

What you'll study

Work based learning (Advanced Professional Practice Module)

You’ll be mentored and supported by a dedicated team with both academic and industry experience to deliver a package of Data Science related work over three trimesters drawing on your current projects/activities.

Three on-campus modules (one per trimester)

(Overall, you’ll attend campus an average of one day per month during term time)

  • Data Driven Decision making
  • Data Analytics
  • Data Wrangling

Software and technologies used will include packages such as: R, Python, Hadoop, Weka, Tableau.

MSc Dissertation

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


Study modules mentioned above are indicative only. Some changes may occur between now and the time that you study.

Full information on this is available in our disclaimer.

Participation in this course will indicate your aspirations as a leading Data Scientist and your dedication to your management, enhancing your chances of promotion. 


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.

Our entry requirements indicate the minimum qualifications with which we normally accept students. Competition for places varies from year to year and you aren't guaranteed a place if you meet the minimum qualifications.

International students

If your qualifications aren't listed above, visit our country pages to get entry requirements for your 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 are committed to being as accessible as possible to anyone who wants to achieve higher education.

Our admissions policies will help you understand our admissions procedures and how decisions are made.


Tuition fees
Students from 2017/18 2018/19
All students - Taught modules *£570 *£890
All students-Dissertation £1,080 tba
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Full MSc cost £4,500 tba
Fees for modules are calculated according to the number of credits (multiples of 20). The rate shown in the table is for 20 credits*.
This course comprises of 180 credits from taught modules and a dissertation. The total fee you will pay is dependant upon the exit award you wish to achieve.

Frequently Asked Questions about Fees
Information of Bursaries and Scholarships

Modules that you will study* as part of this course

Advanced Professional Practice ( SOC11107 )

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 ( SET11122 )

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 ( SET11121 )

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 ( INF11116 )

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.

Executive Masters

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