Online Masters in Data Science – Edinburgh Napier University

Description of module content:

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.

Learning Outcomes for module:
Upon completion of this module you will be able to:
LO1: Critically evaluate the drivers and strategies for advanced analytics and its impact on organisational decision-making
LO2: Critically assess the roles and impact of ethics, governance and professionals in data analysis
LO3: Apply methods of data reduction and of classification to data to identify sub-groups
LO4: Construct and diagnose statistical models to allow prediction of effects and input into strategy development.

Description of module content:
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.

Learning Outcomes for module:
On completion of this module, students will be able to:
LO1: Critically understand the concepts and process of data analytics
LO2: Critically evaluate methods/techniques in data analytics
LO3: Apply data analytics algorithms to datasets to conduct data analysis and visualisation, by using data analytical tools
LO4: Critically interpret and evaluate results generated by analytical techniques
LO5: Investigate current research topics in data analytics

Description of module content:
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.

Learning Outcomes for module:
On completion of this module, students will be able to:
LO1: Critically evaluate the tools and techniques of the data storage, interfacing, aggregation and processing
LO2: Select and apply a range of specialised data types, tools and techniques for data storage, interfacing, aggregation and processing
LO3: Employ specialised techniques for dealing with large data sets
LO4: Design, develop and critically evaluate data driven applications in Python.

Description of module content:

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

Learning Outcomes for module:
Upon completion of this module you will be able to
LO1: Formulate a learning agreement which applies to a live workplace issue at the appropriate level
LO2: Critically evaluate the theories and concepts of the area of study, and show evidence of understanding of the relationship of these concepts to workplace practices
LO3: Apply subject specialist skills to manage and evaluate a major piece of work
LO4: Critically reflect on learning using an appropriate model of reflective practice

Description of module content:
This module provides you with guidance and support towards your completion of an individual research project – a dissertation – whose theme complements your learning on the MSc Online Education programme. Traditionally your dissertation should be a written piece of work, but here, at least one element should be presented as a digital artefact. You may submit a patchwork collection of digital artefacts, in negotiation with your supervisor and examiner, as substantive elements of your work. In either case, work must be the result of independent, critical investigation which evidences the use of relevant research methods and detailed knowledge of established literature and other sources in the chosen area of study. You will be assigned a personal supervisor who is tasked with monitoring your progress as well as offering feedback, advice and guidance. You will also have an identified internal examiner who examines the final dissertation. There are three deliverables prior to final submission: (1) Project Proposal (observing a formal approval process undertaken by the supervisor and student); (2) Initial Report; (3) Outline Dissertation.

Learning Outcomes for module:
Upon completion of this module you will be able to
LO1: Develop extensive knowledge of a specialist area of investigation, and develop and justify research questions appropriate to the area, issues or problems identified
LO2: Critically explore and review literature relevant to the topic chosen
LO3: Underpinned by an identified philosophical position, plan and justify an appropriate academic research methodology appropriate to an in-depth research project
LO4: Manage research and writing schedules, and gather, analyse, synthesise and interpret data with which to explore research questions
LO5: Devise an appropriate form with which to present arguments, findings and conclusions.